MEKAWIRE
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Papers

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

1 papers
TopicAllManipulation · 88VLA · 59Navigation · 52General robotics · 41Robot learning · 32Simulation · 24Perception · 23Humanoid · 18Locomotion · 15Safety · 13Aerial · 4World Action Models · 4World-action model · 3Aerial manipulation · 2Dexterous manipulation · 2Sim-to-Real Manipulation · 2Social Navigation · 2Vision-Language Navigation · 23D Assembly · 13DGS dataset · 15G Simulation · 1AMR Operations · 1Active vision manipulation · 1Adapting a frozen flow-matching VLA by learning only the noise it starts from · 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 · 1Benchmark for adding wrist force sensing to VLA manipulation policies · 1Benchmark for few-shot skill transfer with dexterous robot hands and tactile sensing · 1Bimanual Manipulation · 1Black-box correction of deployed robot policies with world-model planning · 1Byzantine localization · 1Canal inspection · 1Co-speech Humanoid · 1Coding agents that control robots and keep what they learn as reusable skills and lessons · 1Compositional Manipulation · 1Construction navigation · 1Contact-Rich Manipulation · 1Contact-rich Manipulation · 1Continual reinforcement learning on top of robot foundation models · 1Continuum sensing · 1Controlled comparison of how to add world models to VLA robot policies · 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 · 1Faster on-device inference for flow-matching vision-language-action models · 1Federated 3D Perception · 1Fisheye detection · 1Floorplan SLAM · 1Force-aware robot foundation model pretrained on 2,000 hours of human tactile-glove data · 1Geometry-aware manipulation · 1Grasping · 1Grounding · 1Humanoid Control · 1Humanoid RL · 1Humanoid Tool Use · 1Humanoid control, generative modeling of policy weights · 1Humanoid loco-manipulation · 1Humanoid safety · 1Humanoid whole-body control · 1Humanoid whole-body training data from robot-specific video generation · 1Imitation Learning · 1JEPA World Models · 1Lab automation · 1Laboratory Manipulation · 1Latent World Models · 1Legged Manipulation · 1Letting a humanoid revise its cart-pushing plan mid-motion as it learns the cart's mass and wheel constraints · 1Letting humans and coding agents build and tune physics simulations of robot tasks from a phone video · 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 · 1One world model retrained from NVIDIA Cosmos 3 Edge that both predicts scenes and controls 20 robot families · 1Panoramic 3D Detection · 1Panoramic segmentation · 1Pedestrian tracking · 1PersonNav · 1Planning · 1Policy Optimization · 1Predicting contact failures before they happen with a vision, action and force world model · 1Preference Learning · 1Quadruped locomotion · 1Real-time VLA · 1Real-to-sim · 1Real-to-sim-to-real data generation for manipulation · 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 · 1Test-time verification for vision-language-action policies · 1Testing whether failure alarms for robot AI models actually save time once false alarms and reset costs are counted · 1Text-to-3D Policy · 1Tool Co-Design · 1Training care robots that help people stand up and transfer, using simulated humans who move back · 1Training household mobile manipulators with targeted human corrections instead of more demonstrations · 1Trajectory Correction · 1Transferring task structure from human videos to robot world-action models · 1Traversability · 1Turning ordinary human videos into skill-labelled training data for a dexterous robot hand · 1Turning phone-recorded human demonstrations into per-robot post-training data by retargeting object motion · 1Two Unitree G1 humanoids playing table-tennis doubles with learned collision-risk filtering · 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 inference efficiency · 1VLA manipulation · 1VLA robustness · 1VLA robustness benchmark · 1Video world models · 1Vision-language-action models without wrist cameras (learned foveation) · 1Visual Generalization · 1Visual Grounding · 1Visual Reward Models · 1Visuomotor Fusion · 1Visuotactile manipulation · 1Whether labelling messy human demonstrations by quality improves robot foundation models · 1Whole-body mobile manipulation and teleoperation · 1World Models · 1World models · 1World-Action Models · 1compliant humanoid whole-body control · 1data engine converting egocentric human video into replayable bimanual dexterous robot demonstrations · 1dexterous in-hand manipulation with learned world models · 1efficient inference for vision-language-action robot policies · 1humanoid learning from human video · 1humanoid teleoperation with heavy unknown payloads · 1humanoid whole-body manipulation · 1large-scale tactile demonstration dataset and robot-free data collection · 1open vision-language-action model with register-token memory · 1post-training vision-language-action policies for industrial disassembly · 1real-world reinforcement learning for robot policies · 1robot foundation model training · 1robot learning from human video · 1tactile pretraining for vision-language-action models · 1terrain-aware behavioral foundation model for humanoid whole-body control · 1training-signal allocation for vision-language-action robot policies · 1vision-language-action models · 1world models for dexterous manipulation · 1
LevelAnyBeginnerIntermediateExpert
Filtered by technology imitation-learning · clear
arXiv
Training care robots that help people stand up and transfer, using simulated humans who move backIntermediate

Learning Robot Policies for Whole-Body Mobility Assistance via Dynamic Human Motion Generation

Shikun Ban, Shohei Nagai, Qingyang Song, Hanxi Wan, Eric Zhao, Thomas Wang, Zackory EricksonCarnegie Mellon University Robotics Institute, Intuitive Autonomy

A Carnegie Mellon and Intuitive Autonomy preprint trains a care robot almost entirely in simulation and reaches 95.7% success helping a simulated person stand up and 74.0% on lifting and transferring them. The key is a simulated care recipient that reacts to the robot like a real person, and the stand-up skill reached a real robot after just five real demonstrations (6 minutes 26 seconds of data).

Imitation LearningDepth VisionSim-to-Real TransferRL
Read the full paper (PDF)

Topic pages

Manipulation · 88VLA · 59Navigation · 52General robotics · 41Robot learning · 32Simulation · 24Perception · 23Humanoid · 18Locomotion · 15Safety · 13Aerial · 4World 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.