{"total":15,"items":[{"citing_arxiv_id":"2606.31807","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot","primary_cat":"cs.RO","submitted_at":"2026-06-30T15:25:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Reinforcement learning produces a policy for passive inline skating on a humanoid robot that achieves up to 50% lower cost of transport than walking and transfers zero-shot to physical hardware.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.27581","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction","primary_cat":"cs.RO","submitted_at":"2026-06-25T22:13:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SceneBot conditions a humanoid tracking policy on motion references and contact labels, using reconstructed scene-interaction data to unify free-space locomotion with contact-rich manipulation and terrain tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26215","ref_index":16,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes","primary_cat":"cs.RO","submitted_at":"2026-06-24T17:56:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TaskNPoint lets humanoid robots learn dynamic skills such as tennis backhands from single short human video demonstrations plus under one hour of single-GPU simulation training, achieving zero-shot generalization to new goal locations without per-task reward tuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17833","ref_index":36,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning","primary_cat":"cs.RO","submitted_at":"2026-06-16T12:01:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HumanoidArena is a new benchmark of 7 leg-critical HOI/HSI tasks that evaluates egocentric hierarchical whole-body policies in humanoids and finds performance is strongly conditioned on the low-level GMT used.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11628","ref_index":72,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition","primary_cat":"cs.RO","submitted_at":"2026-06-10T03:49:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LUCID learns embodiment-agnostic intent models from unstructured human videos to train dexterous robot policies in simulation, enabling zero-shot transfer on real-world tasks like stirring and wiping.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10449","ref_index":2,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GuideWalk: Learning Unified Autonomous Navigation and Locomotion for Humanoid Robots across Versatile Terrains","primary_cat":"cs.RO","submitted_at":"2026-06-09T05:55:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GuideWalk unifies traversability-aware navigation and terrain-adaptive locomotion into a single policy for humanoid robots via teacher distillation and RL refinement.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09286","ref_index":51,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands","primary_cat":"cs.RO","submitted_at":"2026-06-08T09:52:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"VAIC distills a teacher policy into a vision-and-proprioception student policy using recurrent adaptation and decoupled commands, enabling diverse real-robot tasks like box carrying and skateboarding that outperform baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20645","ref_index":19,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion","primary_cat":"cs.RO","submitted_at":"2026-06-06T10:25:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A multi-channel terrain affordance reward combined with lower-body compliance training via virtual wrenches enables end-to-end PPO-trained humanoid policies to walk at 1 m/s on 0.2 m risers with improved payload robustness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.08059","ref_index":11,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain","primary_cat":"cs.RO","submitted_at":"2026-06-06T08:46:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Perceptive BFM grounds human motion priors in robot terrain perception via terrain-conformal reference synthesis and teacher-student transfer from adapted to raw-reference tracking.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06493","ref_index":44,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers","primary_cat":"cs.RO","submitted_at":"2026-06-04T17:59:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05873","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"LadderMan: Learning Humanoid Perceptive Ladder Climbing","primary_cat":"cs.RO","submitted_at":"2026-06-04T08:47:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A hybrid motion-tracking and imitation-reinforcement pipeline produces a depth-based visuomotor policy that lets humanoids climb varied ladders zero-shot on hardware and perform teleoperated manipulation while climbing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30770","ref_index":17,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World","primary_cat":"cs.RO","submitted_at":"2026-05-29T02:54:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SSR is an end-to-end vision-based framework for humanoid traversal that learns imagined foothold guidance, equivariant latent-space symmetry augmentation, and terrain-specific multi-discriminator motion priors to enable safe locomotion on diverse real-world terrains.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25782","ref_index":38,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"ParkourFormer: Integrating Predictive Supervision and Sequence Modeling into Parkour Locomotion","primary_cat":"cs.RO","submitted_at":"2026-05-25T12:29:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ParkourFormer achieves 93.85% average success on multi-terrain humanoid parkour by fusing Transformer sequence modeling with supervised future-state prediction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15517","ref_index":7,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy","primary_cat":"cs.RO","submitted_at":"2026-05-15T01:27:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough terrain and stairs on the Unitree G1 with onboard computation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13015","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Learning Versatile Humanoid Manipulation with Touch Dreaming","primary_cat":"cs.RO","submitted_at":"2026-04-14T17:54:17+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Liu, and G. Shi, \"Omnih2o: Universal and dexterous human- to-humanoid whole-body teleoperation and learning,\"arXiv preprint arXiv:2406.08858, 2024. [2] Q. Liao, T. E. Truong, X. Huang, Y . Gao, G. Tevet, K. Sreenath, and C. K. Liu, \"Beyondmimic: From motion tracking to versatile humanoid control via guided diffusion,\"arXiv preprint arXiv:2508.08241, 2025. [3] Z. Wu, X. Huang, L. Yang, Y . Zhang, K. Sreenath, X. Chen, P. Abbeel, R. Duan, A. Kanazawa, C. Sferrazzaet al., \"Perceptive humanoid parkour: Chaining dynamic human skills via motion matching,\"arXiv preprint arXiv:2602.15827, 2026. [4] L. Yang, X. Huang, Z. Wu, A. Kanazawa, P. Abbeel, C. Sferrazza, C. K. Liu, R. Duan, and G. Shi, \"Omniretarget: Interaction-preserving"}],"limit":50,"offset":0}