{"total":12,"items":[{"citing_arxiv_id":"2606.05873","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"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":"2604.25459","ref_index":65,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning","primary_cat":"cs.RO","submitted_at":"2026-04-28T10:05:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GS-Playground delivers a high-throughput photorealistic simulator for vision-informed robot learning via parallel physics integrated with batch 3D Gaussian Splatting at 10^4 FPS and an automated Real2Sim workflow for consistent environments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21355","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting","primary_cat":"cs.RO","submitted_at":"2026-04-23T07:14:35+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19344","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input","primary_cat":"cs.RO","submitted_at":"2026-04-21T11:27:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Sparsely gated MoE policies double the success rate of a real Unitree Go2 quadruped on large-obstacle parkour versus matched-active-parameter MLP baselines while cutting inference time compared with a scaled-up MLP.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02911","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Task-Invariant Properties via Dreamer: Enabling Efficient Policy Transfer for Quadruped Robots","primary_cat":"cs.RO","submitted_at":"2026-04-03T09:27:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DreamTIP adds LLM-identified task-invariant properties as auxiliary targets in Dreamer's world model plus a mixed-replay adaptation step, delivering 28.1% average simulated transfer gains and 100% real-world climb success versus 10% for baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"rates physical states such as linear velocity, elevation maps, friction coefficients, center of mass position, and foot contact forces. In real-world evaluation, all methods were deployed and executed directly on the onboard Orin Nano of the Go2 robot, utilizing depth images captured by the D435i camera, which were preprocessed with spatial and temporal filters to mitigate the visual sim-to-real gap [35]. TIP Extractor:In quadruped robot locomotion tasks mentioned in this paper, the TIP generated by the LLM, as shown in the right part of Fig. 2, reveal that tasks such as climb, stairs, and gaps share below critical common constraints: maintaining sufficient terrain clearance to avoid physical collisions, and preserving foot contact stability to prevent slipping and instability."},{"citing_arxiv_id":"2603.04531","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2026-03-04T19:17:42+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.15827","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching","primary_cat":"cs.RO","submitted_at":"2026-02-17T18:59:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A modular system uses motion matching to compose long-horizon human skill chains, trains RL experts, and distills 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":"2602.06382","ref_index":60,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels","primary_cat":"cs.RO","submitted_at":"2026-02-06T04:34:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An end-to-end policy learns robust humanoid locomotion directly from noisy depth images via high-fidelity sensor simulation, vision-aware distillation from privileged maps, and terrain-specific multi-critic reward shaping.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.14617","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"UniCon: A Unified System for Efficient Robot Learning Transfers","primary_cat":"cs.RO","submitted_at":"2026-01-21T03:19:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"UniCon standardizes states and control logic into modular execution graphs for efficient transfer of learning controllers across heterogeneous robots, with lower latency than ROS.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.06571","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input","primary_cat":"cs.RO","submitted_at":"2025-12-06T21:27:50+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.14427","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning","primary_cat":"cs.RO","submitted_at":"2025-11-18T12:32:23+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.18780","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion","primary_cat":"cs.RO","submitted_at":"2025-05-24T16:33:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}