{"as_of":"2026-08-19T16:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d470109219e5519a100a4e32227247d6f464b3ea7c1f046d23e1389992e9266e","coverage":[{"denominator":66,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":66,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T10:08:52.602631Z","state":"measured"},{"denominator":66,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":66,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.11577/citation-record","integrity":"/paper/2606.11577/integrity","json":"/paper/2606.11577/citation-record.json","paper":"/paper/2606.11577"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Integrating image and textual information in human–robot interactions for children with autism spectrum disorder,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:f53e3498ec2a1fb09084beda5fc3872137ae5e3585b426b2f9447229f97e981e","observation_id":"49a0aadc-7afd-44d9-acee-d7cd75980b27","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Task intelligence of robots: Neural model-based mechanism of thought and online motion planning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:31aaa0a9ad9aedbcef176156f79713e7eda60205592babd2ab2cee7ed5ca0f56","observation_id":"eac25f9b-bd19-4fde-a50c-99ded2369eaa","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Scene recognition mechanism for service robot adapting various families: A cnn-based approach using multi-type cameras,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:b6f5ecf66ffdadeaa8c07c9a7c21071ffe919256c70a3dae06af3c83a862b968","observation_id":"ba70902b-9712-4e05-ab46-a07407672419","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"A topological approach to gait generation for biped robots,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:adf5ea860e93148527ccbc25b1bd9c03065d59f386fcc031c892e623f4903a93","observation_id":"1094fe6c-ccd1-44e5-87e3-a19c4eef7c1c","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Motion planning and cooperative manipulation for mobile robots with dual arms,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6588d9d9ae456424495e828f5b3d756ad6b736ad9164f7b78be54683b8d12364","observation_id":"bde29f68-82bf-418b-b04b-d920f036bf2d","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Design and human- robot coupling performance analysis of flexible ankle rehabilitation robot,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:ad590a89845afad3a57774cde2fb92a89fc177f2ec5682628b3d384919ef6b40","observation_id":"d9d09f2c-0494-493c-9b55-a25271e13076","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Promoting trust in industrial human-robot collaboration through preference-based optimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:0b3adce6d00b1e685a1e7f3b8d1720c300c43289c36f82fb6a33aa9eea035241","observation_id":"cb4d40a6-1dd9-4747-af9e-463e06bc6ceb","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Toward safe distributed multi-robot navigation coupled with variational bayesian model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:9f1b58f734f032a9f0117df38925e245414cbe95e34ecd593aa27692523693ec","observation_id":"59b91ac6-5998-48c8-af11-297c1157ae89","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Neuronsgym: A hybrid framework and benchmark for robot navigation with sim2real policy learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:138ac313c58992131850352c08346194e1bf7bea4ab75881779c219ec6fef9b5","observation_id":"9509095b-0d25-4be4-a78c-98b7edfb1264","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Efficient online planning and robust optimal control for nonholonomic mobile robot in unstructured environments,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:53a7bec7bfc2098b094f71828a69aaa5cc02b5dc7d924df5931ef36fade6591b","observation_id":"e077e240-eafc-42ba-8b7b-ffe08cd72fb3","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Toward efficient self-motion- based memory representation for visuomotor navigation of embodied robot,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:4a91f84c1b2095f3eedba16c702ec1302ebbb034a152a2a883a36b36899176ea","observation_id":"d6a2ccde-318c-4541-b7cc-996a10f300d9","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Do you want to make your robot warmer? make it more reactive!","venue":null,"work_id":null,"year":1971},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:05b5d2117e63e729cad73c8d26e4962f958162f1ea19ec2fc7644085e901ea6a","observation_id":"8fd252f7-e175-485c-b919-d8c07433c7af","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Multi-stage cable routing through hierarchical imitation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:406d86a4e197e9f95edf46ea3986fc8b93487cb2e839c213b5d6ddc1731d3ce9","observation_id":"e18e071e-e3f2-40ec-b545-ab23dbd14e07","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"One-shot domain-adaptive imitation learning via progressive learning applied to robotic pouring,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:d6405b0bfea0f7440849f5c415da935d058113421c0a7ff5948f538efacb52ff","observation_id":"0ebe2e71-ae97-4d33-8d9e-ca78c6a61197","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Ranking- based generative adversarial imitation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:706085d50f6b96f3ae18950c5f22b1df1268e062afcf45ad3505413d526d1038","observation_id":"1e837739-6746-44bf-a088-acfcfae174ae","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Learning from demonstrations: A computationally efficient inverse reinforcement learning approach with simplified implementation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:c2b9b41dd82b5ca1790fd077ad66d944c87273be586433bf8751b9a492c278dc","observation_id":"e75b8fc3-8289-4743-b5d1-a833d6dfb17f","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"A survey of imitation learning: Algorithms, recent developments, and challenges,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:239bbc4107a477fe99facbd6c7a9a7d70af3290c456f8eff9f240c76bacfb0b4","observation_id":"f5c196d3-f28d-46e3-8c55-52e725220eca","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Efficient training of artificial neural networks for autonomous navigation,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:dba1bb4895364116bcd6d0daba7e95f1936f1265f5e64c6f31373c2550ed57a6","observation_id":"3a469f40-9c03-42f9-8dee-9e5b7d2ef667","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"A reduction of imitation learning and structured prediction to no-regret online learning,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:4520f9d0b7008ffd47278be0e8e8415c2b442ea29203e49ef36097cdc22dd78c","observation_id":"432bda9c-ce80-4368-823b-095f843986c3","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Is imitation learning the route to humanoid robots?","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:93113cd16290e5f9591bd06fad586931a0c47fd48e460578d73f2bcc0fbc6462","observation_id":"e2c5aca0-53a2-4c63-94cc-269b0fb289aa","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"An algorithmic perspective on imitation learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:73bc47e170f391159cb6a08406c9d9a6eb59a53947db53b59dfc41ee3c4831f8","observation_id":"85a11913-6ef9-409a-bb0c-d958539893a3","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"On robustness of robotic and autonomous systems perception: An assessment of image distortion on state-of-the-art robotic vision model,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6e13d8e6d93317a2b831396a6cbe1472905f7fea9e366a38e93e6bdd0f8bf5ee","observation_id":"d0a188ea-885e-457a-86ce-a026dfb3321e","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"A motion distortion resistant vslam system for quadruped robots based on deep learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6e39b1cb3685075e21aac38f49a1c121892fdd9d6555247c1b78749b44b67ba4","observation_id":"322fcf39-4248-458b-b625-772b03691663","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Image quality assessment in visual reinforcement learning for fast-moving targets,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:111ec952050185a07f3248e87f6e1ce96e509b689cef75b2a59926e509deb866","observation_id":"4a114660-66c9-4e1a-8750-e8a2acdfddce","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:066a7d431e940acb511c3c9a9c3cce701a1888c7d8291e780874a6c19fada479","observation_id":"ee81b28c-2865-4345-931d-8dcddc691f05","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18774","last_updated":"2025-08-18T07:13:27Z","snapshot_observed_at":"2026-08-17T02:03:48.645283Z","submitted_at":"2024-12-25T04:29:22Z","title":"Embodied Image Quality Assessment for Robotic Intelligence","version":3},"cited_work":{"arxiv_id":"2412.18774","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.18774","snapshot_observed_at":"2026-07-03T10:17:57.673134Z","title":"Embod- ied image quality assessment for robotic intelligence,","venue":null,"work_id":"c2faf176-9c02-428e-9ec8-d7151f29c56a","year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"cited_paper":"/paper/2412.18774","citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:bbd1f4af1a3d30bae6014bde5ba10c60f4714066b1c0e88d082b2f8860deed82","observation_id":"5da25d0b-e30d-4d8a-b4ea-8f2684db844c","resolution":{"observed_at":"2026-07-03T10:17:57.674434Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Blind image quality measurement by exploiting high-order statistics with deep dictionary encoding network,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:bac021621784db39acd2e124fd1a8df3722a85d43499cef0983d8562c3241d3e","observation_id":"0521e302-4a91-45f8-b799-639d97194f59","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"A novel rank learning based no-reference image quality assessment method,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:5cdb04bd10da514c40823e68a06804f8a8108cd30fe23ef17a1fd546bac5bd98","observation_id":"51168d3a-f454-4baa-b920-8a0c5d83c9f3","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Medical ultrasound image quality assessment for autonomous robotic screening,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:11db4b2a990c80f813ae8785a83644cc01b3fe64dea5dc38abdb3d85eb56d907","observation_id":"7069e30c-391c-4c57-941c-377eb8defb7e","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Troubleshooting ethnic quality bias with curriculum domain adaptation for face image quality assessment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:7ac1d51030740753400b35904786504a480f29119613278ec92e797ac41d5fe0","observation_id":"37da72b2-db5f-42b1-8766-66d9cf567de5","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Blind image quality assessment: A fuzzy neural network for opinion score distribution prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:d26ad33e44adff5729e8d5637d0e0765358a2625a1f3d85027c3bdc535db8b03","observation_id":"f08780e3-a9ea-48b1-9057-4491d908aa91","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Refining uncertain features with self-distillation for face recognition and person re-identification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:e082a9960d19fd7dc65b8b976e9ed7ee3a8f50700d01f7efcc3289e954c1c98e","observation_id":"c0132c19-8398-4852-acc2-f79cdf999c27","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Clib-fiqa: Face image quality assessment with confidence calibration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:b163eaad901e76cbd7525c5653f6de1a13c97c30621cab416e0316bba4eb99f5","observation_id":"6e0a2558-bc10-445b-84ce-46f5d5c7a6df","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Spatio-temporal feature integration for quality assessment of stitched omnidirectional images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6f1ec14302a182080b04cb81aeefb415e22d8aafdcb3b49bb77d7fba5d45d908","observation_id":"ef3ff0c7-8aad-48f9-a444-badcc5c09c5b","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Unsupervised learning for physical interaction through video prediction,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:16c15f29a69f47d9c469ceaedd94a9430086a4aee7a5545eb5ea2cec2dd54059","observation_id":"60dcfdf3-0718-45fb-bcaa-660e2b5855b3","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6ca3737a293fe4e17aa3120240f667ce6ecbb23bcec39fb0554e9df8646cea60","observation_id":"12a9f655-2fec-4e64-9701-e44371598e4f","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Geneworker: An end-to-end robotic reinforcement learning approach with collaborative generator and worker networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:5f888c81786dca2add79bf68213d5a3347a232c9ec5fb1fae32d51b6015057b6","observation_id":"29c25de9-9def-48aa-915a-00f14d2195b2","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.01691","last_updated":"2022-08-16T16:06:33Z","snapshot_observed_at":"2026-08-18T11:44:22.813405Z","submitted_at":"2022-04-04T17:57:11Z","title":"Do As I Can, Not As I Say: Grounding Language in Robotic Affordances","version":2},"cited_work":{"arxiv_id":"2204.01691","doi":"10.48550/arxiv.2204.01691","metadata_source":"pith","pith_arxiv_id":"2204.01691","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do As I Can, Not As I Say: Grounding Language in Robotic Affordances","venue":"cs.RO","work_id":"037320f1-b0a9-4cbe-a639-bfb25409ce71","year":2022},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"cited_paper":"/paper/2204.01691","citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:ef6588b447b982517c8251dfc5d89754c10de3f3dd52552e2790f0b7765d1921","observation_id":"6e57fda2-3865-47d8-9bc1-b8c8f4accd42","resolution":{"observed_at":"2026-07-03T10:17:57.676579Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06817","last_updated":"2023-08-11T17:45:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-12-13T18:55:15Z","title":"RT-1: Robotics Transformer for Real-World Control at Scale","version":2},"cited_work":{"arxiv_id":"2212.06817","doi":"10.48550/arxiv.2212.06817","metadata_source":"pith","pith_arxiv_id":"2212.06817","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RT-1: Robotics Transformer for Real-World Control at Scale","venue":"cs.RO","work_id":"e11bda85-8531-46bc-a07f-d0ade3643ab1","year":2022},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"cited_paper":"/paper/2212.06817","citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6cdf6c26ed7e02b7de9e538989addcd1bdf11fc56465d71dac309003e7f59f97","observation_id":"1df63c44-6189-42f4-9c33-3ec57d6610fd","resolution":{"observed_at":"2026-07-03T10:17:57.678652Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15818","last_updated":"2023-07-28T21:18:02Z","snapshot_observed_at":"2026-08-02T16:17:50.621617Z","submitted_at":"2023-07-28T21:18:02Z","title":"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control","version":1},"cited_work":{"arxiv_id":"2307.15818","doi":"10.48550/arxiv.2307.15818","metadata_source":"pith","pith_arxiv_id":"2307.15818","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control","venue":"cs.RO","work_id":"ff438a8a-8003-4fae-9131-acd418b3597b","year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"cited_paper":"/paper/2307.15818","citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:687a114501722b8c41d71568fbf3572a15a22ba56311b7c9fdf47e3acf234f95","observation_id":"925224ab-4a03-4e71-a475-4953bec7f812","resolution":{"observed_at":"2026-07-03T10:17:57.681039Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08864","last_updated":"2025-05-14T15:22:36Z","snapshot_observed_at":"2026-08-13T13:59:48.091257Z","submitted_at":"2023-10-13T05:20:40Z","title":"Open X-Embodiment: Robotic Learning Datasets and RT-X Models","version":9},"cited_work":{"arxiv_id":"2310.08864","doi":"10.48550/arxiv.2310.08864","metadata_source":"pith","pith_arxiv_id":"2310.08864","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Open X-Embodiment: Robotic Learning Datasets and RT-X Models","venue":"cs.RO","work_id":"62f0fb6c-e6ae-4dc4-95a4-d9dd64b240e8","year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"cited_paper":"/paper/2310.08864","citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:4789e4877816bf7a6eeed4298e65447af63ce18282014e981ca1dcae38fb0d18","observation_id":"b76b952a-6e32-45f4-a636-0cf1534328b2","resolution":{"observed_at":"2026-07-03T10:17:57.683082Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Metaiqa: Deep meta- learning for no-reference image quality assessment,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:d360fee77da8759d7b746786ab61c6bb8ed84ab92ab739013748472c7b5b6973","observation_id":"62e8843e-fcc7-4981-8166-1457df130eed","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.03548","last_updated":"2018-10-08T16:07:11Z","snapshot_observed_at":"2026-08-17T08:46:14.358166Z","submitted_at":"2018-10-08T16:07:11Z","title":"Meta-Learning: A Survey","version":1},"cited_work":{"arxiv_id":"1810.03548","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.03548","snapshot_observed_at":"2026-07-03T10:17:57.670905Z","title":"Meta-Learning: A Survey","venue":"cs.LG","work_id":"3505be2a-a005-4698-b07d-07bbc23592bf","year":2018},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"cited_paper":"/paper/1810.03548","citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:a7eb1f2e9088513bf2307f07f7e9083b8d6f2e95b45d8538f6a585dde1a322eb","observation_id":"9e6def5b-7278-42d0-8110-f71aa56c0bfd","resolution":{"observed_at":"2026-07-03T10:17:57.672089Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Blindly assess image quality in the wild guided by a self-adaptive hyper network,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:a354e4f9f088e12431a1b2e8284df72d793ee180b108a071d554edf302e2ce09","observation_id":"23d5d797-4ef1-4c53-9225-2f112401ae77","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Forgetting to remember: A scalable incremental learning framework for cross-task blind image quality assessment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:c588d700ada8af27a51e7923f2420fd333688845027d9fe742114d0aaa391e02","observation_id":"38735caa-d34e-419a-9448-27851e83dd70","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"No-reference stereoscopic image quality assessment based on image distortion and stereo perceptual information,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:dd4d377f72c9b77ecdd153c86e042b431cd7405be67ec7fe29fd31f3a15e24d6","observation_id":"b84162ea-b6c9-4466-8d1c-a47a60aad53e","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Vsoiqe: A novel viewport-based stitched 360° omnidirectional image quality evaluator,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:d767ec03b02a321faca042c4f302a9c3d8f53298eb3c6e3e6103abc861d12c17","observation_id":"6529710d-9f29-40f4-b42e-367129d8eb44","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Blind image quality assessment via cross-view consistency,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:dc5c0dfba9265c9181cc34b7aac8eec2580891608cd284392a242351c8c7f2e4","observation_id":"b71977a5-2118-47dd-b232-0e67724af43a","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Perceptual quality assessment of retouched face images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:6e7932f77faaee6afe0dea2670ce275f5e5056196d80ba7a1e8ba6b2bd1eb0ca","observation_id":"49ded24e-498e-4e46-bc1b-3a45387c7977","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Robonet: Large-scale multi-robot learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:32bf243d981ffbc66d12be2c8482a737de0a1888cc1d06314a9f4cc7c15c5300","observation_id":"a23f960b-4401-49e7-82dc-71684810e1a3","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Bridgedata v2: A dataset for robot learning at scale,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:63c8a330bd442a9eb8ef07e813fbd086fc924855eba916625b61e3aa31df79e3","observation_id":"6ed2451d-a982-4cd1-8e31-396bd00314ea","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Interactive language: Talking to robots in real time,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:0a8d23208effd87b9e9cc6efcce336376d4834d77a4ff0c0a473153378b2f166","observation_id":"40ac528a-86ec-4217-a8d6-ccfe938f8c12","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Objective quality assessment of multiply distorted images,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:e3b4aebd175f20b2839fbf0cd5d60e5530d77d8b6987e308f2ca0b492190104b","observation_id":"d6e2ba86-ed6c-4cb7-b609-67552231e4d0","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Hybrid no-reference quality metric for singly and multiply distorted images,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:37ea36e311eaea4612834d41b2fc2f7c6af66f645fd88b965c28f672bb19b619","observation_id":"7a563929-983c-4a8f-9932-1cc52d4bbc75","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Image database tid2013: Peculiarities, results and perspectives,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:2e28abbc9b43b5b8877f9fd21cc3eb54d1b12aed21b00c24849cb00cedd2c2b1","observation_id":"0c5ab762-be28-484a-a25e-f23cc9f82b97","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Kadid-10k: A large-scale artificially distorted iqa database,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:e66f354e9e165a068cc0656738dc6dd7d5ccb88cc35b3af6c541c48aff788c03","observation_id":"4767dc62-3ff7-404f-ae15-fabd41c33f9b","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Degraded reference image quality assessment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:55129c2048a3a7faf50dd86825d959d8a0c23020508ae31f1e2893a445385901","observation_id":"c7838331-0daf-4e68-aae2-de63a25290cc","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:711e5ad470284b0b31497a396b2f435d1247175a931bd0085c78f5b8165e4174","observation_id":"a4a4da40-6eb6-408c-91ce-dc8d66626118","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:a5824c9b02320f5bcd33893636b800ee1cd02d135d908be4e0da2237afdc66bc","observation_id":"63efe8d0-a42b-4dfa-9b31-ef4570f8e4e9","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Policy gradient methods for reinforcement learning with function approximation,","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:43022d90622f4f72e38b10f97760d658beb8909ec9dc346a466d8a205199de09","observation_id":"5311c809-3c6d-4e7e-ad90-4e7669f0d1c7","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Group normalization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:cf9fcbb8aa0adbe797a4db1621d95aa7eb0c5c61490f6477e6182a87efe77255","observation_id":"c7405683-01be-438e-aff2-7a10ee6e22b2","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Distributed representations of words and phrases and their composi- tionality,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:3da510853f5d5d37437a43f804e94182fc8e96c0f5da7b713150ab34cd1951ca","observation_id":"7efe3b7c-fd25-41a6-add2-28848fbffe26","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Image quality assessment: from error visibility to structural similarity,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:34f29f6bd4a263a1f63a651ddd80191fc39488bcd52aa9fff70184e7fa6da703","observation_id":"57ca0b89-a866-43a8-a8dc-626c6f63970e","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"The unreasonable effectiveness of deep features as a perceptual metric,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:addf47feee88ee7d57c5231fe5cab1425fbf8d779208e775088e6b09b1802fb5","observation_id":"05c2d97c-cbc1-4481-a7d9-a40f4075cf1f","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Image quality assessment: Unifying structure and texture similarity,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:b149dee01e3d45dfb619ed7c84528e05589e3da83e530840e5a1f2c39b9c4853","observation_id":"55b77d25-e4c6-4ca1-893b-2927f96dfffc","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T10:08:52.602631Z","title":"Exploring clip for assessing the look and feel of images,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-27T10:08:52.602631Z"},"links":{"citing_paper":"/paper/2606.11577"},"observation_digest":"sha256:bf9fa825dfff1399fae1f5e6ffc13dc784c5d8828d71bf7ff7b2e3041be9dec9","observation_id":"c9d8d674-e6ac-4927-b5ce-c5c523db9c2c","resolution":{"observed_at":"2026-06-27T10:08:52.602631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.11577","last_updated":"2026-06-10T02:07:59Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-06T06:29:15.851662Z","submitted_at":"2026-06-10T02:07:59Z","title":"Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing"},"reference_resolution":{"displayed":66,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":60,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":66},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2606.11577."}