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Paper Citation Record · LEDGER

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

As of 8 August 2026, this Paper Citation Record lists 100 of 104 outbound references and 10 inbound Pith citation observations for arXiv:2502.08643.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.08643 v2

Coverage vector

measured 100 of 104 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:03:16.780089Z

measured 110 of 110 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:57:29.723051Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T06:37:07.457138Z

Reference resolution

100 of 104 outbound references displayed

  • verified exact3
  • verified fuzzy8
  • unresolved89
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b1266be-45f8-4784-acdc-680306b1aa09 · outbound

This paper cites Gpt-4 technical report,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Gpt-4 technical report,

Reference 1

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source=pdf_text observed=2026-08-08T00:03:16.267564Z digest=sha256:a0ca8bc4b8c7a67419ccc487dcce17b139d904d302e6618610ac5325637e4091

Observation 732a8927-91f6-4814-8659-efcd658a3d8c · outbound

This paper cites Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Reference 2

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source=pdf_text observed=2026-08-08T00:03:16.273920Z digest=sha256:1a583dd358c3e89f6c0d96c5c0cabaa43973d317465daf01bc752f37d548b34c

Observation 5d8eb4fa-1ed6-41ac-914e-a9fbc19221b6 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Learning Transferable Visual Models From Natural Language Supervision

Reference 3

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source=pdf_text observed=2026-08-08T00:03:16.280094Z digest=sha256:2d29d570e0c4d34fe34f5bb85a56379039316cb9a06a1a0acf6016062cbb4b0e

Observation ad5ac58d-c798-4e2d-a5c2-99094d62687f · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 4

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source=pdf_text observed=2026-08-08T00:03:16.286143Z digest=sha256:e207fc9dbcbfca84312ccb7036a67a092aba919f53d7a9dd6b68d217d15ee72e

Observation 317fed13-de3b-471c-b25a-0a71ef9299e6 · outbound

This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,

Reference 5

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source=pdf_text observed=2026-08-08T00:03:16.297405Z digest=sha256:3fb2963e0eb816368eaa13a5bee35959b7d306c7504095c5acf081943cc3b0e3

Observation 088d4fc6-afd0-42c9-93a8-351b6d7825e6 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 6

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source=pdf_text observed=2026-08-08T00:03:16.303251Z digest=sha256:0c750a7d8735aeeb6041a36477e096be02b7fac7d73a82732041ec6b076d577c

Observation 142373a9-c858-4b13-9cd2-3eb74d7aae02 · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Flamingo: a Visual Language Model for Few-Shot Learning

Reference 7

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source=pdf_text observed=2026-08-08T00:03:16.308743Z digest=sha256:e667a1d7d4c2b339ef5b946f270ffe4de1f75daddb1766815d43ce1bc42d391f

Observation e8b24053-697c-4782-bd1c-3f03882285ba · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 8

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source=pdf_text observed=2026-08-08T00:03:16.314808Z digest=sha256:989bb4584239c6159ad3751a9dc901d3ddf55cbaa159a542142c10d07f7998d4

Observation d3f89353-8c84-41ef-ae3a-1584c3c226c8 · outbound

This paper cites ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation

Reference 9

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source=pdf_text observed=2026-08-08T00:03:16.320032Z digest=sha256:b8dc983930a5c02c3999185be7b869002843ef72be5500c286d07bde6daa7d6e

Observation e2920c4d-8d7c-4f4c-ad6a-f74eb3245b83 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 10

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source=pdf_text observed=2026-08-08T00:03:16.324781Z digest=sha256:dc833c880f0d8375440f949ef1c26648f1054e29908b69008315ed22d487a02a

Observation 5b8a8ef4-3898-4eb1-8628-278a271f6414 · outbound

This paper cites Code as Policies: Language Model Programs for Embodied Control.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Code as Policies: Language Model Programs for Embodied Control

Reference 11

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source=pdf_text observed=2026-08-08T00:03:16.329340Z digest=sha256:01ffb386ff0a094528e160ac0fbde54239f4cc88c593fdbb3ced309edbdb4777

Observation 555d2eda-675a-4040-be29-929cfa91ba82 · outbound

This paper cites VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Reference 12

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source=pdf_text observed=2026-08-08T00:03:16.334277Z digest=sha256:1c528fe885fc25fde951cd671300e1cb0d79c27e0bb562495ef041d51ddc190e

Observation 662ae966-d8f8-442b-a4d3-1062143c9a09 · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards RT-1: Robotics Transformer for Real-World Control at Scale

Reference 13

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source=pdf_text observed=2026-08-08T00:03:16.339166Z digest=sha256:48f8952aecb442bc0b74c4c872066dbda300f0ebbc4251e7ff2eba1219abffd4

Observation 83199657-d3b9-4475-b670-f7445c5e3894 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 14

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source=pdf_text observed=2026-08-08T00:03:16.344340Z digest=sha256:d479a05897d96426df2776815c9f9a374e222116d7f725fa3bd47796931f6f42

Observation 8892c62c-f0fb-4578-9e79-6e293228ce01 · outbound

This paper cites MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting

Reference 15

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source=pdf_text observed=2026-08-08T00:03:16.349095Z digest=sha256:e495b62b5f261a1295e0bd2db52874cb0e8e10b461f92ab727f6ebe68733562a

Observation 529ffc25-9927-46a7-a709-d8d606eb9dcb · outbound

This paper cites CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models

Reference 16

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source=pdf_text observed=2026-08-08T00:03:16.353903Z digest=sha256:1934c153bbd504db3944d53afca00af07c312da0a26087b966c5018aa9569cfc

Observation 7cfb61bf-3992-4246-8236-c50008ec1e4b · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Octo: An Open-Source Generalist Robot Policy

Reference 17

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source=pdf_text observed=2026-08-08T00:03:16.358600Z digest=sha256:9da13f5f57aa4567d17eb40ab1faadd571d47fe47b6bebbc2158c5413ed34858

Observation 4373253c-8b85-4c23-9d85-3f86929dab2c · outbound

This paper cites Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

Reference 18

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source=pdf_text observed=2026-08-08T00:03:16.363277Z digest=sha256:72d1c39e9f08bb0ad11872b419310361d35c0e8f819ff8fa5bb293ff3eac8ddd

Observation b6a4f8a9-fda5-4db4-a2c9-3f87bb869704 · outbound

This paper cites Creative Robot Tool Use with Large Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Creative Robot Tool Use with Large Language Models

Reference 19

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source=pdf_text observed=2026-08-08T00:03:16.367942Z digest=sha256:f225a24e915ac72a5260aea5535651655cc7f6682fbd21d34219f91121799cdb

Observation 0653a99c-f2a4-4786-9ffe-584c89d177be · outbound

This paper cites Generalizable Long-Horizon Manipulations with Large Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Generalizable Long-Horizon Manipulations with Large Language Models

Reference 20

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source=pdf_text observed=2026-08-08T00:03:16.373020Z digest=sha256:daa80bf95bf893fffab7a77fa2e631360c3e0911c497951c84b0b51a847314c4

Observation 5b4a0c47-36b8-4475-a554-5451989a4704 · outbound

This paper cites PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Reference 21

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source=pdf_text observed=2026-08-08T00:03:16.378325Z digest=sha256:95a08011d9ab2a0e907566a07ca21b6dc98b4d242d901ff2967dcea68d99ab97

Observation a83dc631-d7b4-4afe-ab66-230b13764f82 · outbound

This paper cites Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics

Reference 22

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source=pdf_text observed=2026-08-08T00:03:16.383871Z digest=sha256:22df62b5bfbf128cc295640d1b25cda8587b1afe0005994f28dd25a162466a2d

Observation ed59c352-b145-4bba-8d31-3040c9a2daa1 · outbound

This paper cites Large language models for robotics: A survey,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Large language models for robotics: A survey,

Reference 23

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source=pdf_text observed=2026-08-08T00:03:16.389090Z digest=sha256:f188aa6125fa390a11d1d005afc7af8250864d7fd67d496d02a8caeb2230f575

Observation d943c6f6-736e-4c43-85b7-6eefa4b85dc4 · outbound

This paper cites Distilling and retrieving generalizable knowledge for robot manipulation via language corrections,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Distilling and retrieving generalizable knowledge for robot manipulation via language corrections,

Reference 24

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source=pdf_text observed=2026-08-08T00:03:16.393865Z digest=sha256:1be244034b143783b3629ade6b5dad91193df029acdcda57a216ae693e7e1026

Observation 19238c07-5158-45b6-b551-47101acad255 · outbound

This paper cites How to prompt your robot: A promptbook for manipulation skills with code as policies,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards How to prompt your robot: A promptbook for manipulation skills with code as policies,

Reference 25

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source=pdf_text observed=2026-08-08T00:03:16.398731Z digest=sha256:f13f2446d8390ee2795d835a819bf175221de88059dc41688586260f4dfe2db8

Observation 3da5c2a2-ffd0-452c-9a01-2929b080cd96 · outbound

This paper cites Generative expressive robot behaviors using large language models,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Generative expressive robot behaviors using large language models,

Reference 26

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source=pdf_text observed=2026-08-08T00:03:16.403571Z digest=sha256:dd9980ba3966a3a4505d4246611a44194da39d05d94df21e1498233895875df7

Observation 664f3558-b44e-49a5-8a93-9ed2c2f2dadd · outbound

This paper cites Learning to Learn Faster from Human Feedback with Language Model Predictive Control.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Learning to Learn Faster from Human Feedback with Language Model Predictive Control

Reference 27

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.408366Z digest=sha256:07e50d47228eb101a61c300eff10c6efcfd4ffc8333b9622913c97da152d06fa

Observation 1f5651fc-e0eb-4dfe-99bd-811acf0b9be5 · outbound

This paper cites Grounded decoding: Guiding text generation with grounded models for embodied agents,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Grounded decoding: Guiding text generation with grounded models for embodied agents,

Reference 28

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source=pdf_text observed=2026-08-08T00:03:16.413592Z digest=sha256:f53de1dbde3168674e4c850afb2e737b2afc0ad383f4339a99c4257958b9b871

Observation f07379f8-3bc7-49e1-b5ee-ac82877c3989 · outbound

This paper cites Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners

Reference 29

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source=pdf_text observed=2026-08-08T00:03:16.418242Z digest=sha256:7339a2722d1a9e031a28568dfd70fce9745f1fc35dfedaec12bf4f05a1822f55

Observation d8d02fa8-5955-4d62-a16b-aff96c21dac7 · outbound

This paper cites VIMA: General Robot Manipulation with Multimodal Prompts.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards VIMA: General Robot Manipulation with Multimodal Prompts

Reference 30

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source=pdf_text observed=2026-08-08T00:03:16.423650Z digest=sha256:e8aaad39201d540a493fb66186ee10bd05a291d53a3b8a53693c2a4a7224e554

Observation 2efc599c-5a43-49c0-8a75-ce0d06ab43bd · outbound

This paper cites Guiding Long-Horizon Task and Motion Planning with Vision Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Guiding Long-Horizon Task and Motion Planning with Vision Language Models

Reference 31

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source=pdf_text observed=2026-08-08T00:03:16.429020Z digest=sha256:d0e27d9f5a1f2233d03a2a9ada16f7336b2e0a9aedd8b3d5ac5cb8c160f5477b

Observation 96103a68-ba73-4019-9b32-e39a14f13af7 · outbound

This paper cites AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation

Reference 32

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source=pdf_text observed=2026-08-08T00:03:16.434203Z digest=sha256:44716dbd5152d281436225ae074328b43bdc98c19368be84a2215f3f70da672a

Observation 44dc6a78-0b65-48c8-bf06-bc23b98a7823 · outbound

This paper cites Manipulate-Anything: Automating Real-World Robots using Vision-Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 33

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source=pdf_text observed=2026-08-08T00:03:16.439194Z digest=sha256:ad865c6e3494eabe9742e6fb6ef74466f8fd4d64db499f0dd68daa8ae2a223c8

Observation 8ea0237c-f5f4-453d-96ec-09e0aa7d84b6 · outbound

This paper cites RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics

Reference 34

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source=pdf_text observed=2026-08-08T00:03:16.443621Z digest=sha256:e7bbb13c254ab987682c2bcdffb01bffe97ff76a685fea2052ef55ac79aa189e

Observation 58b5b6b3-2faf-4a72-b4b4-41e25a9211d5 · outbound

This paper cites Progprompt: program genera- tion for situated robot task planning using large language models,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Progprompt: program genera- tion for situated robot task planning using large language models,

Reference 35

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Observation 007f0ff7-ce39-4135-9000-08b40544ad0a · outbound

This paper cites KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data

Reference 36

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Observation 99f53cd6-4de4-4efb-a43d-0f792ba826d3 · outbound

This paper cites Eurekaverse: Environment Curriculum Generation via Large Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Eurekaverse: Environment Curriculum Generation via Large Language Models

Reference 37

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source=pdf_text observed=2026-08-08T00:03:16.457081Z digest=sha256:0da83cc3d1422b604ddb349176b3f0e517fdcc7157102e9283e7c2d13e056185

Observation 15488d81-4889-416c-84ba-099316a576f3 · outbound

This paper cites Robotic Control via Embodied Chain-of-Thought Reasoning.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Robotic Control via Embodied Chain-of-Thought Reasoning

Reference 38

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source=pdf_text observed=2026-08-08T00:03:16.462109Z digest=sha256:60791b9bf4b0b57c8df968707abc2f8c91f50d9655c57da3778122e9c3556183

Observation 5ed50014-dc07-4ba7-9b2c-2fb866fddf9a · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 39

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Observation 4394fe00-d420-4333-a55f-b6681f54234c · outbound

This paper cites Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,

Reference 40

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source=pdf_text observed=2026-08-08T00:03:16.471416Z digest=sha256:c232b151669dfc4f9fb4e0272be9685d1f364989437c6155a84a686d8c105020

Observation 33f15191-450c-470f-9b63-ab03f410c084 · outbound

This paper cites RT-H: Action Hierarchies Using Language.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards RT-H: Action Hierarchies Using Language

Reference 41

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Observation 1e5ec352-5dd6-4eae-b5b9-624776d59a88 · outbound

This paper cites Vlmpc: Vision-language model predictive control for robotic manipulation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Vlmpc: Vision-language model predictive control for robotic manipulation,

Reference 42

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source=pdf_text observed=2026-08-08T00:03:16.480335Z digest=sha256:f0ded2fde8bcb94163f8715b078f024fb1e36c624bd65a9f01e01ab5a2f69120

Observation b5c28687-3fbd-496b-865e-d78dc4aa2b1e · outbound

This paper cites Language to Rewards for Robotic Skill Synthesis.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Language to Rewards for Robotic Skill Synthesis

Reference 43

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source=pdf_text observed=2026-08-08T00:03:16.484627Z digest=sha256:f8f006465ac2dc78cbac3ed3b68b7aea2bdfe6823e424f610f5422682a393794

Observation 62608a93-1a11-4020-9270-b67231964b8e · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 44

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source=pdf_text observed=2026-08-08T00:03:16.489420Z digest=sha256:009c166e9cb9bbf41362d4f20fd1fb84410bf9253109b4a153f85299be08025c

Observation c29644e2-6885-45e1-bf47-879082ef0151 · outbound

This paper cites DrEureka: Language Model Guided Sim-To-Real Transfer.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards DrEureka: Language Model Guided Sim-To-Real Transfer

Reference 45

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source=pdf_text observed=2026-08-08T00:03:16.494550Z digest=sha256:1c258fac2757bf165cd2aad3b0d84c41992236ea4aeadbda724dce6730559d83

Observation a102b346-d097-4ca6-aa9d-2dbaf6820c14 · outbound

This paper cites Text2Reward: Reward Shaping with Language Models for Reinforcement Learning.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Text2Reward: Reward Shaping with Language Models for Reinforcement Learning

Reference 46

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source=pdf_text observed=2026-08-08T00:03:16.500490Z digest=sha256:6e5534b3b75a0bb37d351844ca32088b06c61c621e9b79ffc268b96a2121f573

Observation 7d63cf55-2465-4972-a54b-66ad03d96750 · outbound

This paper cites Real- time perception meets reactive motion generation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Real- time perception meets reactive motion generation,

Reference 47

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source=pdf_text observed=2026-08-08T00:03:16.505774Z digest=sha256:ba7808652d5a5e01d95ffaa90011db58ba050ad9401de51f107932587c034c55

Observation 75742f90-7033-4095-97f9-d0ba89f41c3a · outbound

This paper cites You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

Reference 48

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source=pdf_text observed=2026-08-08T00:03:16.510856Z digest=sha256:98c632ea2d00cb7cb39397cf96d8237aa46a241bd668de65bb382bbcf8494d24

Observation bb9f0c69-932f-457a-a79d-73effc0e24cc · outbound

This paper cites One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion,

Reference 49

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source=pdf_text observed=2026-08-08T00:03:16.516080Z digest=sha256:decc901edee86a5a6ad9594fcff6e7c370d3f5c0b6632e7582ec9f561942f6af

Observation 3d0621d7-2005-48c6-883b-fb227736d911 · outbound

This paper cites Sparp: Fast 3d object reconstruction and pose estimation from sparse views,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Sparp: Fast 3d object reconstruction and pose estimation from sparse views,

Reference 50

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source=pdf_text observed=2026-08-08T00:03:16.521047Z digest=sha256:e11196fd56ae9936445ad0de232479d0d2a17d7ba04e80cc3b72809c81287645

Observation 07e10d96-a663-4f26-b5af-33c8d94f09ac · outbound

This paper cites Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

Reference 51

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source=pdf_text observed=2026-08-08T00:03:16.525937Z digest=sha256:0823472caabd15ec3029808760f7672795fee93a1e544cc7dc6ba4bc300d5c00

Observation 1b538cba-dfac-41b6-b9ca-fd4557694eca · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Zero-1-to-3: Zero-shot one image to 3d object,

Reference 52

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source=pdf_text observed=2026-08-08T00:03:16.531439Z digest=sha256:87e93897089b9b39eaf854337659ca30cbf1917aba3228589adfaa74417d4aa9

Observation e6d82dd1-e8e4-41ab-9a62-a308126c2e00 · outbound

This paper cites Get3d: A generative model of high quality 3d textured shapes learned from images,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Get3d: A generative model of high quality 3d textured shapes learned from images,

Reference 53

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source=pdf_text observed=2026-08-08T00:03:16.536582Z digest=sha256:96c5d2861142f751eb3dba2e3dbb0aae14601551673a104dec957249c1a3538f

Observation 04e37a37-102c-4b70-a3fd-4d0045dd6b5b · outbound

This paper cites A-sdf: Learning disentangled signed distance functions for articulated shape representation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards A-sdf: Learning disentangled signed distance functions for articulated shape representation,

Reference 54

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source=pdf_text observed=2026-08-08T00:03:16.542036Z digest=sha256:eaf79bc89d2e28bb9dcfe0689bc660e2bf2dadae76b57875bb90737ceb2c8bfd

Observation 2e9f0d80-8d8e-4350-86eb-36970cd86470 · outbound

This paper cites Ditto: Building digital twins of articulated objects from interaction,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Ditto: Building digital twins of articulated objects from interaction,

Reference 55

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source=pdf_text observed=2026-08-08T00:03:16.546629Z digest=sha256:58dedf5336235326367ef65b7c7d25877901d4d65bbf1c41a974882449eb9c13

Observation 9caf3985-cbf1-4e30-9a4a-6a1388b17753 · outbound

This paper cites Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery

Reference 56

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source=pdf_text observed=2026-08-08T00:03:16.551554Z digest=sha256:7c90f1acdf9b784bcbd417549bf86ef3dc442cea5c323565edd79614794f2b14

Observation a4010356-25d8-479e-9c86-8c4a65b6fa4d · outbound

This paper cites URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images

Reference 57

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source=pdf_text observed=2026-08-08T00:03:16.556855Z digest=sha256:7f9990aa8d259566a981bde1e69eae92807ec9a703b2e6712fa5248f85f5524a

Observation 3d333903-7b7c-4472-bdc8-e844087814d8 · outbound

This paper cites Real2Code: Reconstruct Articulated Objects via Code Generation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Real2Code: Reconstruct Articulated Objects via Code Generation

Reference 58

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source=pdf_text observed=2026-08-08T00:03:16.561525Z digest=sha256:4f66ceffc127eb708bfbcc2373d8fa56af13889bd5b0bd50dfae7376595ef64d

Observation af77e4de-66e1-4859-884d-d40d7ef01dea · outbound

This paper cites Paris: Part-level recon- struction and motion analysis for articulated objects,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Paris: Part-level recon- struction and motion analysis for articulated objects,

Reference 59

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source=pdf_text observed=2026-08-08T00:03:16.566123Z digest=sha256:c4cb564fb19a1c76bb94d2a8b65c65c5425eeba6a1a6546b552acb11289ea9b1

Observation 4e786dbc-32f1-41a2-b17a-6c567ed5a538 · outbound

This paper cites Cage: Con- trollable articulation generation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Cage: Con- trollable articulation generation,

Reference 60

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source=pdf_text observed=2026-08-08T00:03:16.570709Z digest=sha256:d7a1a0a6a6ef24869fed90faaeb8d125f0c588c1e248651143ef54cfcc11e226

Observation ca7c229c-7f2f-4a2c-98fd-5453666e2ab8 · outbound

This paper cites SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects

Reference 61

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source=pdf_text observed=2026-08-08T00:03:16.575359Z digest=sha256:c2e05c8f6bc159a29637d593dc4aa7591272c05950def540313b28b1da1d8ca4

Observation 5b03311d-30e7-43f3-9407-336d5d316d1b · outbound

This paper cites Occlusion-aware recon- struction and manipulation of 3d articulated objects,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Occlusion-aware recon- struction and manipulation of 3d articulated objects,

Reference 62

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source=pdf_text observed=2026-08-08T00:03:16.580461Z digest=sha256:30a688e19f28fadd751d4ad545045a238916a6371819c314d79906b5d95bcab4

Observation a1d0c1b3-e377-423b-b87f-bc088945bc0b · outbound

This paper cites Bundlesdf: Neural 6-dof tracking and 3d reconstruction of unknown objects,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Bundlesdf: Neural 6-dof tracking and 3d reconstruction of unknown objects,

Reference 63

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source=pdf_text observed=2026-08-08T00:03:16.585255Z digest=sha256:876dadb2d85e63edb3de53c51fd594afc562516f74922dda4ed5c6261275607e

Observation 4df0e853-1037-410a-815f-0e420d0edc9b · outbound

This paper cites CLIPort: What and Where Pathways for Robotic Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards CLIPort: What and Where Pathways for Robotic Manipulation

Reference 64

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source=pdf_text observed=2026-08-08T00:03:16.590139Z digest=sha256:411a84ba9f2418371c28338f99cceaec7b1ec567f23b5df676acd33f638f549d

Observation 0fd02d25-9392-45e1-8798-4480d4e5219f · outbound

This paper cites Transic: Sim-to-real policy transfer by learning from online correction,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Transic: Sim-to-real policy transfer by learning from online correction,

Reference 65

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source=pdf_text observed=2026-08-08T00:03:16.595186Z digest=sha256:cf06507f9a7617f5b279d003c52b92f04b530bedd82cf894fd07bb23ccfe7a0e

Observation b0bff8fe-78e9-4604-9ac7-f9efefd56a88 · outbound

This paper cites Multi-skill Mobile Manipulation for Object Rearrangement.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Multi-skill Mobile Manipulation for Object Rearrangement

Reference 66

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source=pdf_text observed=2026-08-08T00:03:16.605432Z digest=sha256:0467aeff6e8a037a93fd0d0d0167cdb3206e1f6b1031c19d7964c88b26a87db4

Observation a5f2b335-7871-45e3-8dd5-056af89c77c5 · outbound

This paper cites HomeRobot: Open-Vocabulary Mobile Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards HomeRobot: Open-Vocabulary Mobile Manipulation

Reference 67

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source=pdf_text observed=2026-08-08T00:03:16.610183Z digest=sha256:d48d72b83ed997633dd064be42d810688c3b50602dc2378b3386987e11bbb309

Observation b3806a66-12bf-4dfb-b5ca-eebb8f5602b6 · outbound

This paper cites Dynamic Handover: Throw and Catch with Bimanual Hands.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Dynamic Handover: Throw and Catch with Bimanual Hands

Reference 68

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source=pdf_text observed=2026-08-08T00:03:16.615179Z digest=sha256:5ff0c98fb7db93b99bea7551c2638fcc8728c6753e859ee7d73d40acd72561d2

Observation 0410bcb5-b1dd-429a-82ee-633ace0a45f0 · outbound

This paper cites Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation

Reference 69

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source=pdf_text observed=2026-08-08T00:03:16.620521Z digest=sha256:8a0ebb349b522d67ee9a918ceb300a2c6fa41a3a05270cd26293abe5234f5823

Observation 470fc6e2-cae2-4a70-9afc-2fbe1116d420 · outbound

This paper cites DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation

Reference 70

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source=pdf_text observed=2026-08-08T00:03:16.625626Z digest=sha256:177eda5b265385bc3da829bb1c0b536262639832cd29df60affbc0d0d708bd52

Observation 41f75bc4-2731-4128-aff6-73f73c6e75d6 · outbound

This paper cites In-hand object rotation via rapid motor adaptation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards In-hand object rotation via rapid motor adaptation,

Reference 71

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.630924Z digest=sha256:5487c4f336910c858ec471c5555834f2e0c052414b07bf32fe59e7d8f45cf38b

Observation c51f514b-42a6-4fa3-b125-8258f75defa5 · outbound

This paper cites Rotating without Seeing: Towards In-hand Dexterity through Touch.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Rotating without Seeing: Towards In-hand Dexterity through Touch

Reference 72

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.635725Z digest=sha256:94c6c6a40dbb815912c99ca2ef429439d1f3bdef46d48b41796000c4dc970737

Observation 2267b1a2-56e4-446a-9802-32275240a9e6 · outbound

This paper cites RMA: rapid motor adaptation for legged robots,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards RMA: rapid motor adaptation for legged robots,

Reference 73

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.640594Z digest=sha256:23403ed5f923407b33ea89ad63560fcdf6f9d4c7abde8be8b53f0d3899666c06

Observation 8af8501f-9335-4193-939f-17cf7b24bf87 · outbound

This paper cites Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion

Reference 74

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.645397Z digest=sha256:7f20f8d2c8f1a500b54908576c3c26af971771a48c5185337af3f1a1d5cb5748

Observation 44239ca9-7d85-4244-aada-2d6212c54b4e · outbound

This paper cites Sim-to-Real: Learning Agile Locomotion For Quadruped Robots.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Reference 75

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.650419Z digest=sha256:4c41332d3ccb347c8b7c80f0e32ceaf8aa0b144250e94a7cc1afb3ef0c67bbc8

Observation 3e08b070-9a30-4f22-ada5-63d07d0d8c4b · outbound

This paper cites Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation

Reference 76

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T00:03:16.655603Z digest=sha256:4ebf880224826681f193a47a887c2f5df3024039c6239e25a3ab71c0a0dc21e4

Observation c63dd2d0-66f6-49f5-b0db-96a9bfaaa7d5 · outbound

This paper cites Planar Robot Casting with Real2Sim2Real Self-Supervised Learning.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Planar Robot Casting with Real2Sim2Real Self-Supervised Learning

Reference 77

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.661092Z digest=sha256:4261e4a949eb3bebcbd0020978b568ab898edf32666e1dfa8e34bd8caab4fa18

Observation 6a6e2641-724a-494a-a6d3-e5e4f7539323 · outbound

This paper cites Using simulation and domain adaptation to improve efficiency of deep robotic grasping,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Using simulation and domain adaptation to improve efficiency of deep robotic grasping,

Reference 78

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source=pdf_text observed=2026-08-08T00:03:16.666271Z digest=sha256:80a6e617ef555aba9807c7b499f04912ccc653728a78ee5625fa34b696000df8

Observation 9b86c0da-750c-4ebe-b4d5-90e5f209480c · outbound

This paper cites Meta Reinforcement Learning for Sim-to-real Domain Adaptation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Meta Reinforcement Learning for Sim-to-real Domain Adaptation

Reference 79

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.671206Z digest=sha256:88cadb3f5a1dc285ba966d586f33dee257bfcd98d108efe4d1409aee07caaaf2

Observation df8ff7a0-8582-47d5-880c-0db70b1480a6 · outbound

This paper cites Rl-cyclegan: Reinforcement learning aware simulation-to-real,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Rl-cyclegan: Reinforcement learning aware simulation-to-real,

Reference 80

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raw_fallback, observed 2026-08-08T00:03:33.543582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.676293Z digest=sha256:838c8a83fd472162594d6c30011f1d789d78a5c0a7346823686cc42ad49259d2

Observation 655ca09d-0f59-4ea9-b68e-a20f6599dff3 · outbound

This paper cites Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to- canonical adaptation networks,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to- canonical adaptation networks,

Reference 81

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raw_fallback, observed 2026-08-08T00:03:33.527122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.691287Z digest=sha256:91c2367e963c59853beb3e2c4ba989ad500f03ab809d575ecb800bd1e270feab

Observation 918868db-59eb-4783-8598-70603d5f4824 · outbound

This paper cites Bayesian imi- tation learning for end-to-end mobile manipulation,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Bayesian imi- tation learning for end-to-end mobile manipulation,

Reference 82

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.695854Z digest=sha256:495e26f80fe2ee1b70b7cc1b51882b984db5f6b752fe4a2ff698512144141583

Observation b5666bd5-45f2-45b0-b831-78a7b5ec4c90 · outbound

This paper cites Solving Rubik's Cube with a Robot Hand.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Solving Rubik's Cube with a Robot Hand

Reference 83

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.700270Z digest=sha256:f0d5308c2517c5587e69eea0c7f0262f9015523e83ab5e68e0abf488ad87bb4f

Observation 5f4da67b-ac22-4494-bd3d-7abc12bf1fdb · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Domain randomization for transferring deep neural networks from simulation to the real world,

Reference 84

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.705325Z digest=sha256:fccb189151b65e14bd018475ebbe5e825ab7ddfc6d6a84b4ee0108503d787a46

Observation 939e7cb9-bde7-4797-8727-41a56f1648e5 · outbound

This paper cites BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym

Reference 85

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local_arxiv, observed 2026-08-08T00:03:16.973687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.709726Z digest=sha256:d83abd8b1b944f67688ec7ab5f35c14ccd3096714f734ff7f9e9364d6a8ae074

Observation fc9229a3-c9f4-4ddd-a822-f74f97c35a72 · outbound

This paper cites Learning quadrupedal locomotion over challenging terrain,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Learning quadrupedal locomotion over challenging terrain,

Reference 86

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.714378Z digest=sha256:793438a747a3e2bcf9dcbc455e4a9da355f54b0a5082479c79d5636b22c30c16

Observation 83685c33-80c8-43bc-8796-64bb8bf8a06f · outbound

This paper cites Sim-to- real transfer of robotic control with dynamics randomization,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Sim-to- real transfer of robotic control with dynamics randomization,

Reference 87

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.718797Z digest=sha256:b1e4f5f626aa338dbc6ea6f663e91d2be05fbb980ff221f0ec21b4ad7fca76eb

Observation c7875af2-470e-413c-bf9d-ae829df6fec6 · outbound

This paper cites Closing the sim-to-real loop: Adapting simulation randomization with real world experience,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Closing the sim-to-real loop: Adapting simulation randomization with real world experience,

Reference 88

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raw_fallback, observed 2026-08-08T00:03:33.364032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.723039Z digest=sha256:dd3814e50507690cd9ca0ba282033ffc6c907a309b01bfb97038cc407cae4cd5

Observation 96cb6d3b-45f7-4b44-9cc0-5449c84b15c6 · outbound

This paper cites Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation

Reference 89

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.727408Z digest=sha256:457e585b9b0de06c398d44d45a04d885b6566fe2fce971d696b2c9704e7bca98

Observation 2511b640-3fc8-4757-8395-1306a854eb46 · outbound

This paper cites FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

Reference 90

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source=pdf_text observed=2026-08-08T00:03:16.732128Z digest=sha256:e7e010aa5c337e46f18b6805d9ccf13edb84b0c83a81fab27f2b13f69a1e5724

Observation a0154d1e-f285-4fe7-88e9-b2abef40519c · outbound

This paper cites Isaac gym: High performance gpu-based physics simu- lation for robot learning,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Isaac gym: High performance gpu-based physics simu- lation for robot learning,

Reference 91

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raw_fallback, observed 2026-08-08T00:03:33.291338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.736463Z digest=sha256:23252fb1397b24fb9286fb363545ad0bb5e9c463e44be0bc7a13f0907b62b7f1

Observation 9946b09c-0387-4146-b78a-7fac5a80ad38 · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Proximal Policy Optimization Algorithms

Reference 92

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source=pdf_text observed=2026-08-08T00:03:16.741065Z digest=sha256:fd66b15f57ddc1096bbaaa8f798eebc907438765ee7443166b72e3bc344daf37

Observation db9a0076-e3d1-47fc-ac81-de6d76c7216d · outbound

This paper cites Actor-critic algorithms,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Actor-critic algorithms,

Reference 93

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source=pdf_text observed=2026-08-08T00:03:16.745497Z digest=sha256:9fe0145dfedce14f74949e87e6f42dc88b4d92b7bd07ad4eda9f22e0e423ea51

Observation fb03dff6-e79c-4f1b-a6a7-497ce90976d9 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 94

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source=pdf_text observed=2026-08-08T00:03:16.749999Z digest=sha256:72bfc4fe9d29cea129036b8390824d1ac980e87ee9f60bf46af890a696da16e7

Observation 48235b7d-9590-4daf-9aa1-02d1cd9200c4 · outbound

This paper cites Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,

Reference 95

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source=pdf_text observed=2026-08-08T00:03:16.755082Z digest=sha256:a20e4747eea705433f6988bf298f7d57346b987171dae88ddc88dc26c3e25d00

Observation e19f28ed-b16e-4dd8-9bbe-ad8ce9ad5440 · outbound

This paper cites Deep reinforce- ment learning for robotic manipulation with asynchronous off-policy updates,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Deep reinforce- ment learning for robotic manipulation with asynchronous off-policy updates,

Reference 96

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raw_fallback, observed 2026-08-08T00:03:33.203701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-08T00:03:16.759988Z digest=sha256:c213bce5fd1b0169440322aec4fbd30529c34309aaa392e2c1eb616f794f1252

Observation 5d116be6-bf3c-4fa1-a6dd-ff8cc3b1431a · outbound

This paper cites Data-efficient Deep Reinforcement Learning for Dexterous Manipulation.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

Reference 97

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.764736Z digest=sha256:7430907b4cceeae59fe2006f52008c43f5342a7caa889564650ddacae128f664

Observation fd0364ba-02a4-4b1c-ac53-44544d5f3b75 · outbound

This paper cites Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Reference 98

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source=pdf_text observed=2026-08-08T00:03:16.769641Z digest=sha256:510e615d825d3a9974d24ed62e214c9dc55bf821f012f2826962c91dcd0324ab

Observation a106ce4d-ad29-4721-87e4-0e11f1c83468 · outbound

This paper cites Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 99

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:03:16.774897Z digest=sha256:a45e9eb19c9248a6410a6c72b2901d7b0f9e9e34c98746fd44c095eb82191936

Observation 88bac22c-7408-4409-97af-615540a10443 · outbound

This paper cites Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Spatialvlm: Endowing vision-language models with spatial reasoning capabilities,

Reference 100

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source=pdf_text observed=2026-08-08T00:03:16.780089Z digest=sha256:f9e6789eb32de54c103179b9aa3f8647c7e44b9ab909bda7490fff77077f27b9

Pith citing papers

Observation 680fdbc5-9dcc-42c6-94f7-1772805f9740 · inbound

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making cites this paper.

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 55

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:57:29.723051Z digest=sha256:6eb7d6a457d2ad60849e733b0aaac56069ca6838ffd5b3b50f631dd24aead3ee

Observation b1375379-f124-451c-aa10-bf2537dc0f4e · inbound

T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models cites this paper.

T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.998541Z digest=sha256:fbc6ee83e500a648a6a99e80347feb8a1b699bea894f6d13fe6811cc821f25be

Observation cbb5d694-b31c-47df-841f-462af41c88b1 · inbound

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations cites this paper.

Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 92

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arxiv_id, observed 2026-05-19T06:37:07.459812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T06:36:13.144868Z digest=sha256:7a7d725aa5a9dc41cda25b8cb15ee13c2c425901d64071dc47c8a37bb46c430d

Observation c3bd9742-0f67-46a1-9bb5-8663222f2895 · inbound

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training cites this paper.

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:06.245250Z digest=sha256:348b65277574aa93c122a831a16d698626c6b66c12fe65766695363eb7970854

Observation e48e9bbf-228b-45ee-9df6-127ffd6cfe68 · inbound

"Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth cites this paper.

"Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:15:42.737737Z digest=sha256:05b0bfde91878f3a310427f191e430ae0551513738c9517f03024920cc6d03aa

Observation 16a0106c-88aa-4b60-a40b-59df9e41021c · inbound

VLM4D: Towards Spatiotemporal Awareness in Vision Language Models cites this paper.

VLM4D: Towards Spatiotemporal Awareness in Vision Language Models A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 65

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unresolved
no resolver link, observed 2026-08-06T05:12:37.457514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:12:37.457514Z digest=sha256:1fd167763e80b137b7e6b85d576928c9557fb437544765d692d6f0df8bae5bec

Observation 05f18a08-58d8-41cb-a3d9-e8e08043ca47 · inbound

IGen: Scalable Data Generation for Robot Learning from Open-World Images cites this paper.

IGen: Scalable Data Generation for Robot Learning from Open-World Images A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-17T02:58:54.973570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-17T02:58:36.214948Z digest=sha256:48c401496a10c9525d343ffadeb5bf773c0af82c98b7dabbc67164447aaf0ce3

Observation b803f424-e42c-467b-9638-48bffd3de407 · inbound

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation cites this paper.

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-03T05:38:49.656397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:38:49.656397Z digest=sha256:40394a717711db009465d82723fc57dec2b3511aaa3ad6791a60006a641239dd

Observation 688f2f83-d14a-4173-8092-2f5a8882a798 · inbound

Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation cites this paper.

Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 46

Resolution
unresolved
no resolver link, observed 2026-07-14T06:33:12.974578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T06:33:12.974578Z digest=sha256:6b973b4fe2445f316749e0787507c8e77162356c41464b937904eb8875c4d14a

Observation fabdcdc1-322a-45ad-9cbf-33661fd7b2f2 · inbound

LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks cites this paper.

LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards

Reference 23

Resolution
unresolved
no resolver link, observed 2026-07-30T20:27:56.604936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T20:27:56.604936Z digest=sha256:60b498c690c96153eb9157b807bea91ba0a1c6bc826181e1aa123937f2835f64