{"total":19,"items":[{"citing_arxiv_id":"2605.22272","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors","primary_cat":"cs.RO","submitted_at":"2026-05-21T10:15:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Imagine2Real enables zero-shot humanoid-object interaction by unifying motions as 4D point trajectories, tracking only base/hands/object keypoints inside a BFM latent space, and training with progressive simple rewards for mocap deployment.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.05110","ref_index":26,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts","primary_cat":"cs.RO","submitted_at":"2026-05-06T16:43:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"M. Kitani, C. Liu, and G. Shi, \"Omnih2o: Universal and dexterous human-to- humanoid whole-body teleoperation and learning,\" inConference on Robot Learning. PMLR, 2025, pp. 1516-1540. [25] Z. Chen, M. Ji, X. Cheng, X. Peng, X. B. Peng, and X. Wang, \"Gmt: General motion tracking for humanoid whole-body control,\"arXiv preprint arXiv:2506.14770, 2025. [26] T. He, J. Gao, W. Xiao, Y . Zhang, Z. Wang, J. Wang, Z. Luo, G. He, N. Sobanbab, C. Panet al., \"Asap: Aligning simulation and real-world physics for learning agile humanoid whole-body skills,\"arXiv preprint arXiv:2502.01143, 2025. [27] Z. Li, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, \"Reinforcement learning for versatile, dynamic, and robust bipedal"},{"citing_arxiv_id":"2605.03452","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-05T07:35:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"track complex full-body motions and transfer the learned be- haviors from simulation to real hardware [29]-[32]. Inspired by these advances, we design our own learned whole-body controller (WBC) as the execution layer of BifrostUMI. After the desired robot motion is obtained by SKR and inverse kinematics, it is executed by the WBC trained in MJLab [33]. The WBC tracks a short horizon of robot-native full-body reference motion, consisting of the root pose and the 29-DoF joint configuration, while maintaining dynamic consistency and robustness to sim-to-real discrepancies. At each high-level update, the reference motion is repre- sented as a motion chunk Mref = n pr t ,q r t ,q j t oT t=1 ,(2) wherep r"},{"citing_arxiv_id":"2605.01518","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids","primary_cat":"cs.RO","submitted_at":"2026-05-02T16:16:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VOFA combines a high-level visuomotor policy with a low-level force-adaptive controller to let humanoids push objects up to 17 kg to arbitrary goals using only noisy onboard vision, achieving over 80% real-world success.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25459","ref_index":16,"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":1,"top_context_role":"other","top_context_polarity":"unclear","context_text":"InProceedings of the Computer Vision and Pattern Recognition Conference, pages 21537-21546, 2025. [15] Alex Hanson, Allen Tu, Vasu Singla, Mayuka Jayaward- hana, Matthias Zwicker, and Tom Goldstein. Pup 3d-gs: Principled uncertainty pruning for 3d gaussian splatting. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 5949-5958, 2025. [16] Tairan He, Jiawei Gao, Wenli Xiao, Yuanhang Zhang, Zi Wang, Jiashun Wang, Zhengyi Luo, Guanqi He, Nikhil Sobanbab, Chaoyi Pan, et al. Asap: Aligning simula- tion and real-world physics for learning agile humanoid whole-body skills.arXiv preprint arXiv:2502.01143, 2025. [17] Tairan He, Zi Wang, Haoru Xue, Qingwei Ben, Zhengyi Luo, Wenli Xiao, Ye Yuan, Xingye Da, Fernando"},{"citing_arxiv_id":"2604.21355","ref_index":13,"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":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"RPG trains a single policy with transition and timing randomization for stable multi-skill fighting on humanoids, integrated with locomotion for arbitrary-duration combat.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21351","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot","primary_cat":"cs.RO","submitted_at":"2026-04-23T07:10:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The Weightlessness Mechanism lets humanoid robots imitate non-self-stabilizing motions by dynamically relaxing specific joints to exploit passive environmental contacts, generalizing from single demonstrations to varied setups.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20841","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation","primary_cat":"cs.CV","submitted_at":"2026-04-22T17:59:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DeVI enables zero-shot physically plausible dexterous control by imitating synthetic videos via a hybrid 3D-human plus 2D-object tracking reward.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14834","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Switch: Learning Agile Skills Switching for Humanoid Robots","primary_cat":"cs.RO","submitted_at":"2026-04-16T10:11:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Switch enables humanoid robots to perform agile, seamless transitions between locomotion skills via a kinematic skill graph, DRL tracking policy, and real-time graph-search scheduler.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10351","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Trajectory-based actuator identification via differentiable simulation","primary_cat":"cs.RO","submitted_at":"2026-04-11T21:36:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Differentiable simulation enables torque-sensor-free actuator model identification from trajectory data, achieving 1.88x better position tracking than a stand-trained baseline and 46% longer travel in downstream locomotion policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07331","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"RoSHI: A Versatile Robot-oriented Suit for Human Data In-the-Wild","primary_cat":"cs.RO","submitted_at":"2026-04-08T17:48:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RoSHI is a hybrid wearable that combines sparse IMUs and egocentric SLAM to capture accurate full-body 3D pose and shape data in natural environments for robot learning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.11758","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model","primary_cat":"cs.RO","submitted_at":"2026-02-12T09:34:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HAIC enables robust humanoid interactions with underactuated objects by predicting their dynamics from proprioceptive history and using a world model for adaptive control.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.03205","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control","primary_cat":"cs.RO","submitted_at":"2026-02-03T07:18:01+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HUSKY combines humanoid-skateboard dynamics modeling with adversarial motion priors and physics-guided lean-to-steer strategies to achieve real-world stable skateboarding on a humanoid robot.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.14617","ref_index":9,"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":33,"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":"2511.22963","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary","primary_cat":"cs.RO","submitted_at":"2025-11-28T08:11:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Humanoid-LLA converts unconstrained natural language commands into stable whole-body motions for humanoid robots using a unified motion vocabulary and two-stage supervised-plus-reinforcement fine-tuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.07820","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control","primary_cat":"cs.RO","submitted_at":"2025-11-11T04:37:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.25241","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors","primary_cat":"cs.RO","submitted_at":"2025-10-29T07:48:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.08241","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion","primary_cat":"cs.RO","submitted_at":"2025-08-11T17:55:26+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BeyondMimic combines compact motion tracking with a unified guided latent diffusion model to master diverse agile behaviors from human demos and solve unseen downstream tasks via test-time classifier guidance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}