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Papers

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

1 papers

TopicAll Manipulation · 41 VLA · 28 Navigation · 16 Perception · 16 Learning · 13 Robotics · 13 Humanoid · 12 Safety · 7 Simulation · 7 Locomotion · 5 World Action Models · 4 Aerial manipulation · 2 Dexterous manipulation · 2 Sim-to-Real Manipulation · 2 Social Navigation · 2 Vision-Language Navigation · 2 World models · 2 World-action model · 2 3D Assembly · 1 3DGS dataset · 1 5G Simulation · 1 AMR Operations · 1 Active vision manipulation · 1 Adaptive MPC · 1 Adaptive action horizons · 1 Adaptive intervention · 1 Aerial · 1 Aerial Manipulation · 1 Aerial navigation · 1 Agricultural Robotics · 1 Articulation Estimation · 1 Autonomous Driving · 1 Autonomous Exploration · 1 Battery-aware RL · 1 Belief-space planning · 1 Bimanual Manipulation · 1 Byzantine localization · 1 Canal inspection · 1 Co-speech Humanoid · 1 Compositional Manipulation · 1 Construction navigation · 1 Contact-Rich Manipulation · 1 Contact-rich Manipulation · 1 Continuum sensing · 1 Curriculum RL · 1 Dexterous Manipulation · 1 Distributed Manipulation · 1 Distribution Shift · 1 Driving Simulation · 1 Efficient VIO · 1 Egocentric data · 1 Egocentric video · 1 Embodied Agents · 1 Embodied policy · 1 Empowerment MPC · 1 Endoscopic navigation · 1 Endovascular robotics · 1 Event-camera VO · 1 Exoskeletons · 1 Failure recovery · 1 Federated 3D Perception · 1 Fisheye detection · 1 Floorplan SLAM · 1 Geometry-aware manipulation · 1 Grasping · 1 Grounding · 1 Humanoid Control · 1 Humanoid RL · 1 Humanoid Tool Use · 1 Humanoid control · 1 Humanoid loco-manipulation · 1 Humanoid safety · 1 Imitation Learning · 1 JEPA World Models · 1 Lab automation · 1 Laboratory Manipulation · 1 Latent World Models · 1 Legged Manipulation · 1 LiDAR Security · 1 Lifelong RL · 1 MAPF · 1 MAV guidance · 1 Manipulation LfD · 1 Manipulation Safety · 1 Marine simulation · 1 Memory-centric VLA · 1 Miniature Locomotion · 1 Mining Perception · 1 Mobile manipulation · 1 Model-based RL · 1 Monocular SSC · 1 Motion Planning · 1 Multi-Agent RL · 1 Multi-Robot Exploration · 1 Multi-Robot Manipulation · 1 Multi-UAV Exploration · 1 Multi-UAV State Estimation · 1 Multi-agent RL · 1 Multi-agent motion planning · 1 Multi-camera odometry · 1 Multi-fidelity RL · 1 Multi-robot manipulation · 1 Multi-skill Manipulation · 1 Multi-view perception · 1 Multimodal simulation · 1 Navigation & Mapping · 1 Object Pose Estimation · 1 Object Search · 1 Panoramic 3D Detection · 1 Panoramic segmentation · 1 Pedestrian tracking · 1 PersonNav · 1 Planning · 1 Policy · 1 Policy Optimization · 1 Preference Learning · 1 Quadruped locomotion · 1 Real-time VLA · 1 Real-to-sim · 1 Representation alignment · 1 Robot-Use Agents · 1 Runtime Verification · 1 Safe Generative Planning · 1 Safe RL-MPC · 1 Safe VLA · 1 Safety monitoring · 1 Safety-critical control · 1 Sim-to-real Navigation · 1 Sim-to-real manipulation · 1 Simulation geometry · 1 Skill Discovery · 1 Social navigation · 1 Soft Pneumatic Actuators · 1 Soft manipulation · 1 Sonar SLAM · 1 Streaming 3D reconstruction · 1 Submovement decomposition · 1 Surgical robotics · 1 Tactile · 1 Tactile Exploration · 1 Tactile VLA · 1 Temporal manipulation · 1 Terrain traversability · 1 Text-to-3D Policy · 1 Tool Co-Design · 1 Trajectory Correction · 1 Traversability · 1 UAV Navigation · 1 UAV Swarms · 1 UAV traffic monitoring · 1 Underwater Robotics · 1 Underwater active mapping · 1 Underwater perception · 1 Unified world-action model · 1 VLA Conditioning · 1 VLA Defense · 1 VLA Robustness · 1 VLA evaluation · 1 VLA manipulation · 1 VLA robustness · 1 Video world models · 1 Visual Generalization · 1 Visual Grounding · 1 Visual Reward Models · 1 Visuomotor Fusion · 1 Visuotactile manipulation · 1 World Models · 1 World-Action Models · 1

LevelAny Beginner Intermediate Expert

Filtered by technology reinforcement-learning · clear

Oct 11, 2026arXiv

HumanoidExpert

P-BFM: Perceptive Behavioral Foundation Models for Humanoid Control via Unsupervised Reinforcement Learning

Xue Wang, Yitang Li, Yiming Zhang, Siqin Wang, Junhan Sun, Zhihao Liu, Yaojie Shen, Jingbo Yuan, Hao Zhao, Qingdu Li

Behavioral foundation models (BFMs) provide humanoids with reusable repertoires of whole-body behaviors; in real-world humanoid control, the same behavioral intent may require fundamentally different motions under different terrain constraints. Covering such variation with terrain-matched demonstrations requires enumerating a combinatorial space of behaviors and terrain geometries.

Humanoid Foundation

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Topic pages

Manipulation · 41 VLA · 27 Navigation · 16 Perception · 16 Robot learning · 13 General robotics · 13 Humanoid · 12 Safety · 7 Simulation · 7 Locomotion · 4 World Action Models · 5 Aerial manipulation · 3 Dexterous manipulation · 3 Sim-to-Real Manipulation · 3 Social Navigation · 3

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