{"total":14,"items":[{"citing_arxiv_id":"2606.31912","ref_index":10,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing","primary_cat":"cs.RO","submitted_at":"2026-06-30T16:16:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Foot-mounted proximity sensors provide pre-contact feedback that, when integrated into RL, improves quadruped traversal robustness on discrete terrain with reliable sim-to-real transfer.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25765","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal 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and perform teleoperated manipulation while climbing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04718","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation","primary_cat":"cs.RO","submitted_at":"2026-06-03T10:51:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30770","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SSR: Scaling Surefooted and Symmetric 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them into a depth-based policy that lets a Unitree G1 humanoid autonomously climb, vault, and roll over obstacles up to 1.25 m tall.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.04831","ref_index":86,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning","primary_cat":"cs.RO","submitted_at":"2025-11-06T21:43:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"arXiv:2505.11164, 2025. 30 [84] Fereshteh Sadeghi and Sergey Levine. CAD2RL: Real single-image flight without a single real image. Robotics: Science and Systems (RSS), 2017. 27 [85] Clemens Schwarke, Mayank Mittal, Nikita Rudin, David Hoeller, and Marco Hutter. RSL-RL: A learning library for robotics research.arXiv preprint arXiv:2509.10771, 2025. 24, 30 [86] Antonio Serrano-Munoz, Dimitrios Chrysostomou, Simon Bøgh, and Nestor Arana-Arexolaleiba. skrl: Modular and flexible library for reinforcement learning.Journal of Machine Learning Research (JMLR), 24(254):1-9, 2023. 24 50 Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning [87] Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor."}],"limit":50,"offset":0}