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Research

Papers

New robotics and physical-AI papers, with what each one means for the industry.

3 papers
TopicAllManipulation · 76VLA · 65Navigation · 35General robotics · 32Robot learning · 27Perception · 22Humanoid · 21Simulation · 15Locomotion · 13Safety · 11World Action Models · 4World-action model · 3Aerial · 2Aerial manipulation · 2Dexterous manipulation · 2Sim-to-Real Manipulation · 2Social Navigation · 2Vision-Language Navigation · 23D Assembly · 13DGS dataset · 15G Simulation · 1AMR Operations · 1Active vision manipulation · 1Adaptive MPC · 1Adaptive action horizons · 1Adaptive intervention · 1Aerial Manipulation · 1Aerial navigation · 1Agricultural Robotics · 1Articulation Estimation · 1Autonomous Driving · 1Autonomous Exploration · 1Battery-aware RL · 1Belief-space planning · 1Bimanual Manipulation · 1Byzantine localization · 1Canal inspection · 1Co-speech Humanoid · 1Compositional Manipulation · 1Construction navigation · 1Contact-Rich Manipulation · 1Contact-rich Manipulation · 1Continual reinforcement learning on top of robot foundation models · 1Continuum sensing · 1Cross-embodiment transfer of humanoid whole-body controllers · 1Curriculum RL · 1Dexterous Manipulation · 1Distributed Manipulation · 1Distribution Shift · 1Driving Simulation · 1Efficient VIO · 1Egocentric data · 1Egocentric video · 1Embodied Agents · 1Embodied policy · 1Empowerment MPC · 1Endoscopic navigation · 1Endovascular robotics · 1Evaluating robot world models against real executions · 1Event-camera VO · 1Exoskeletons · 1Failure recovery · 1Federated 3D Perception · 1Fisheye detection · 1Floorplan SLAM · 1Geometry-aware manipulation · 1Grasping · 1Grounding · 1Humanoid Control · 1Humanoid RL · 1Humanoid Tool Use · 1Humanoid loco-manipulation · 1Humanoid safety · 1Humanoid whole-body control · 1Imitation Learning · 1JEPA World Models · 1Lab automation · 1Laboratory Manipulation · 1Latent World Models · 1Legged Manipulation · 1LiDAR Security · 1Lifelong RL · 1MAPF · 1MAV guidance · 1Manipulation LfD · 1Manipulation Safety · 1Marine simulation · 1Memory-centric VLA · 1Miniature Locomotion · 1Mining Perception · 1Mobile manipulation · 1Model-based RL · 1Monocular SSC · 1Motion Planning · 1Multi-Agent RL · 1Multi-Robot Exploration · 1Multi-Robot Manipulation · 1Multi-UAV Exploration · 1Multi-UAV State Estimation · 1Multi-agent RL · 1Multi-agent motion planning · 1Multi-camera odometry · 1Multi-fidelity RL · 1Multi-robot manipulation · 1Multi-skill Manipulation · 1Multi-view perception · 1Multimodal simulation · 1Navigation & Mapping · 1Object Pose Estimation · 1Object Search · 1Panoramic 3D Detection · 1Panoramic segmentation · 1Pedestrian tracking · 1PersonNav · 1Planning · 1Policy Optimization · 1Preference Learning · 1Quadruped locomotion · 1Real-time VLA · 1Real-to-sim · 1Reinforcement learning for industrial assembly · 1Representation alignment · 1Robot-Use Agents · 1Runtime Verification · 1Safe Generative Planning · 1Safe RL-MPC · 1Safe VLA · 1Safety monitoring · 1Safety-critical control · 1Sim-to-real Navigation · 1Sim-to-real manipulation · 1Simulation geometry · 1Skill Discovery · 1Social navigation · 1Soft Pneumatic Actuators · 1Soft manipulation · 1Sonar SLAM · 1Streaming 3D reconstruction · 1Submovement decomposition · 1Surgical · 1Surgical robotics · 1Tactile Exploration · 1Tactile VLA · 1Temporal manipulation · 1Terrain traversability · 1Text-to-3D Policy · 1Tool Co-Design · 1Trajectory Correction · 1Traversability · 1UAV Navigation · 1UAV Swarms · 1UAV traffic monitoring · 1Underwater Robotics · 1Underwater active mapping · 1Underwater perception · 1Unified world-action model · 1VLA Conditioning · 1VLA Defense · 1VLA Robustness · 1VLA evaluation · 1VLA manipulation · 1VLA robustness · 1Video world models · 1Visual Generalization · 1Visual Grounding · 1Visual Reward Models · 1Visuomotor Fusion · 1Visuotactile manipulation · 1World Models · 1World models · 1World-Action Models · 1
LevelAnyBeginnerIntermediateExpert
Filtered by technology sim-to-real · clear
arXiv
Cross-embodiment transfer of humanoid whole-body controllersExpert

TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments

Qingyu Lan, Zhihao Tian, Zifan Wang, Junwei LiangThe Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology

A HKUST team reports in a preprint that it moved SONIC, a whole-body controller trained for Unitree's 1.32 m G1, onto the full-size 1.82 m Unitree H2 in about eight hours on one GPU, against the roughly 21,000 GPU-hours SONIC cost to train. If the result holds on more robots, humanoid makers could reuse one expensive motion controller across product generations instead of retraining from scratch.

RLSim-to-Real TransferTeleoperationRobot Foundation Models
HumanoidFoundationUnitree RoboticsNVIDIA
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arXiv
Sim-to-Real ManipulationExpert

RoboBridge: A Self-Evolving Embodied Agent Framework for Sim-to-Real Transfer

Chenxi Li, Zhangrui Zhao, Rui Li, Yuan Gao, Kehui Liu, Jiarui Li, Dong Wang, Tong Si, Minting Pan, Wanli Ouyang, Dongzhan Zhou

Preprint. RoboBridge is a framework that represents manipulation tasks as editable procedural skills and wraps a frozen vision-language-action policy as a guided action tool, evolving those skills from execution feedback. On LIBERO-PRO it reaches 74.4% overall success, beating the strongest reported baseline at 72.1%; on three real-robot tasks, 10 episodes of adaptation lift success from 9/15, 7/15, 7/15 to 15/15, 14/15, 15/15. This shifts sim-to-real transfer from retraining to selective skill revision, cutting adaptation iterations by 25% and token use by about 75%.

Sim-to-Real Transfer
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arXiv
Humanoid whole-body controlExpert

CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments

Tan-Dzung Do, Tuan Dat Phuong, Nico Bohlinger, Cuc T. Trinh, Siwei Ju, Vien Anh Ngo, Jan Peters, Xinchao Wang, An T. LeVinRobotics, National University of Singapore, TU Darmstadt, VinUniversity, DFKI, Hessian AI

A whole-body controller that lets a humanoid imitate a dance, reach a pose or chase a reward from a single prompt normally takes more than 100 GPU-hours to train for each robot. This preprint from VinRobotics, NUS and TU Darmstadt carries the controller built for Unitree's G1 over to Booster's T1, Fourier's N1 and an in-house robot called M3 for under an hour of shared encoder training plus under ten GPU-hours per robot, with tracking almost as accurate as a dedicated controller.

Robot Foundation ModelsRLSim-to-Real Transfer
Unitree RoboticsBooster RoboticsFourier
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Topic pages

Manipulation · 76VLA · 65Navigation · 35General robotics · 32Robot learning · 27Perception · 22Humanoid · 21Simulation · 15Locomotion · 13Safety · 11World Action Models · 5World-action model · 3Aerial manipulation · 3Dexterous manipulation · 3Sim-to-Real Manipulation · 3Social Navigation · 3

Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.