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New robotics and physical-AI papers, with what each one means for the industry.
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TopicAllManipulation · 83Navigation · 51General robotics · 41VLA · 36Robot learning · 31Simulation · 24Perception · 23Locomotion · 13Safety · 13Humanoid · 10Aerial · 4World Action Models · 4World-action model · 3Aerial manipulation · 2Sim-to-Real Manipulation · 2Social Navigation · 2Vision-Language Navigation · 23D Assembly · 13DGS dataset · 15G Simulation · 1A 100-hour wearable dataset of people walking and working with their hands, recorded as video, whole-body motion, finger motion and touch for humanoid training · 1A 4-billion-parameter perception model that points at and boxes objects precisely, used as the visual backbone for robot manipulation and driving policies · 1A benchmark and 3.1k-demonstration dataset that tests whether humanoid robot policies can pick the right tool and then use it, standing still and while walking, on simulated and real Unitree G1 robots · 1A controlled comparison of discrete-token and continuous action heads for single-task policies and PaliGemma-based VLA models, and why discrete heads fail or succeed · 1A frontier language model controlling a Franka arm learns from its own failed attempts by tracing late failures back to earlier decisions, with no demonstrations and no weight updates · 1A general chat-style vision-language model drives a robot arm through mid-level commands and live feedback, with no robot training data · 1A lightweight verifier that predicts the consequences of a VLA's planned actions and decides how many of them to execute before looking again · 1A physics-based benchmark from KAIST that checks whether vision-language-action robot models crush or deform the objects they pick up, not only whether they finish the task · 1A reinforcement-learning reaching controller trained only in Isaac Lab that guides a UR10e arm to the stem of hydroponic lettuce without touching the growing channels · 1A residual correction layer that keeps the size, energy and posture of humanoid gestures such as waving, bowing and crouching when an existing whole-body tracker executes them · 1A safety layer that decides, at every control step, whether a flipping humanoid should finish the move, abort and land on its feet, or fall in a way that protects its head and hands · 1A training add-on for VLA models such as OpenVLA-OFT and pi0.5 that keeps, rather than discards, background and lighting cues and lifts success under unseen conditions · 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 · 1Adding a small latent world model to a pi0.5 policy so that predicted consequences of a planned action condition the action generator, without video generation or large-scale video pretraining · 1Adding a tactile expert to the pi0.5 vision-language-action model so a robot learns contact-heavy tasks such as wiping, inserting and picking up chips from 50 demonstrations, without a separate tactile pretraining stage · 1Aerial navigation · 1Agricultural Robotics · 1An object-centric VLA that tracks each object's identity and state and learns to predict how objects will move under the robot's actions · 1Articulation Estimation · 1Audit of seven robot-learning benchmarks finds 22 bugs and 4 design flaws that make sped-up VLA models look better than the original · 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 · 1CapPro lets a pi0.5 robot policy attempt a new manipulation task after watching one human video, without new robot demonstrations or fine-tuning · 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 · 1Cross-hand dexterous learning: a matched seven-hand, six-task simulation benchmark comparing action representations for one shared policy · 1Curriculum RL · 1Dexterous manipulation · 1Diagnosing why VLA robots fail at long household tasks that require moving around, and fixing planning, base positioning and grasping with agentic tools around an unchanged VLA · 1Distributed Manipulation · 1Distribution Shift · 1Driving Simulation · 1Efficient VIO · 1Egocentric data · 1Egocentric video · 1Embodied Agents · 1Embodied policy · 1Empowerment MPC · 1End-to-end drone navigation from stereo RGB images only, trained in simulation and flown on real hardware without fine-tuning · 1Endoscopic navigation · 1Endovascular robotics · 1Evaluating robot policies without running them: fitted Q-evaluation on offline trajectories with a DINOv3 encoder and a vision-language success detector, against video world-model evaluators · 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 · 1Fine-tuning pretrained robot policies such as NVIDIA's GR00T N1.7 on a few demonstrations without losing robustness to new camera views, backgrounds and objects · 1Fisheye detection · 1Floorplan SLAM · 1Force-aware robot foundation model pretrained on 2,000 hours of human tactile-glove data · 1Geometry-aware manipulation · 1Giving a VLA policy memory by feeding it compact latent codes of what changed between consecutive camera frames · 1Grasping · 1Humanoid control, generative modeling of policy weights · 1Humanoid loco-manipulation · 1Humanoid whole-body control · 1Humanoid whole-body training data from robot-specific video generation · 1Imitation Learning · 1JEPA World Models · 1Lab automation · 1Laboratory Manipulation · 1Language-to-motion for humanoids: event-grounded diffusion that generates Unitree G1 motion and edits it turn by turn · 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 a robot's vision-language-action model improve during deployment by reading its own recent attempts and rewards, without retraining any weights · 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 · 1Marine simulation · 1Memory-centric VLA · 1Miniature Locomotion · 1Mining Perception · 1Mobile manipulation · 1Model-based RL · 1Monocular SSC · 1Motion Planning · 1Motion quality of VLA-driven arms: replaying identical commands and network delays through different low-level controllers to measure jerk against tracking error · 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 reinforcement-learning policy that walks a 55 kg industrial quadruped over gaps, platforms, grating stairs and beams straight from raw LiDAR points, with no terrain map · 1One reinforcement-learning policy that walks a Unitree Go2 with a 6-DoF arm and places the gripper to within 3.22 cm under simulated lunar gravity, with safety limits enforced during training · 1One world model retrained from NVIDIA Cosmos 3 Edge that both predicts scenes and controls 20 robot families · 1Open-weights VLA family for dual-arm robots: embodiment midtraining before task finetuning, plus online correction from a few human episodes · 1Panoramic 3D Detection · 1Panoramic segmentation · 1Pedestrian tracking · 1PersonNav · 1Personalising a pretrained vision-language-action model to one home or office from a 20 to 30 minute phone scan, using a Gaussian-splat digital twin to generate pickup demonstrations automatically · 1Planning · 1Policy Optimization · 1Post-training VLA policies on messy real data: a reachability graph scores which action chunks move a precise assembly task forward · 1Predicting contact failures before they happen with a vision, action and force world model · 1Preference Learning · 1Pretraining a delivery robot's navigation model on 10,000 hours of first-person human walking video by predicting scene structure instead of pixels, then adapting it with 17 hours of robot driving · 1Quadruped locomotion · 1Real-time VLA · 1Real-to-sim · 1Real-to-sim-to-real data generation for manipulation · 1Real-world reinforcement learning that splits a dexterous robot hand's finger skill from the arm's coordination policy, so tool-use tasks such as working a power drill or a pipette can be learned in about an hour · 1Reinforcement learning for industrial assembly · 1Representation alignment · 1Robot-Use Agents · 1Runtime Verification · 1Runtime recovery for a frozen VLA policy: a latent world model forecasts whether to keep going, move the camera or replan the arm · 1Runtime safety for VLA policies: a training-free barrier filter that guards the gripper, wrist and forearm against a moving hazard on edge hardware · 1Safe Generative Planning · 1Safe RL-MPC · 1Safe VLA · 1Safety monitoring · 1Safety-critical control · 1Scissor-shaped handheld teaching tool that lets people feel grip forces while recording robot demonstrations, used to fine-tune pi0.5 on dual-arm Franka and Flexiv robots · 1Shrinking a 3.3-billion-parameter robot foundation model into a 451-million-parameter student that runs in two inference steps · 1Sim-to-real Navigation · 1Sim-to-real manipulation · 1Simulation geometry · 1Skill Discovery · 1Social navigation · 1Soft Pneumatic Actuators · 1Soft manipulation · 1Sonar SLAM · 1Splitting each two-handed robot action into separate left and right tokens that share a timestamp, so a pi0.5-based policy coordinates two dexterous arm-hand systems better · 1Starting a robot policy's flow-matching generation from its own recent actions and frames instead of random noise, so vision-language-action and world-action models train in fewer epochs and act in one sampling step · 1Streaming 3D reconstruction · 1Submovement decomposition · 1Surgical · 1Surgical robotics · 1Tactile Exploration · 1Teaching a Unitree G1 humanoid martial-arts jumps and spins from video of national-level athletes, with fall recovery learned inside the same policy · 1Teaching a real humanoid to return tennis balls using public walking data and ordinary tennis videos instead of new motion capture · 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 · 1Text-to-motion for humanoids: a body-part-aware latent diffusion model that writes Unitree G1 joint references a frozen tracker can execute · 1Tool Co-Design · 1Training a VLA for a drone with a robot arm entirely on synthetic trajectories, then chaining local skills across sites with a scene graph to complete multi-step missions · 1Training a vision-language-action model to predict the physical effect of its next actions instead of the raw motor commands, then decoding actions from that effect · 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 first-person human demonstrations into training data a humanoid can use directly, so a vision-language-action model trained without any robot demonstrations can carry objects about 10 m on a Unitree G1 · 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 · 1Turning recorded human hand-object demonstrations into physically valid robot-hand trajectories with one shared reinforcement-learning policy instead of one optimisation per clip · 1Turning third-person human videos of table-tennis serves, pickups and ladder climbing into Unitree G1 skills by preserving where and how fast contacts happen · 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 · 1Using a learned multi-view world model as the training environment for reinforcement learning on a pi0.5 policy, so the robot improves with little extra real-world interaction · 1Using a video generator to turn four real clips of a person carrying a box into 256 synthetic interaction videos, then into simulation training data for a humanoid that picks up, carries and drops unseen objects · 1VLA Conditioning · 1VLA Defense · 1VLA Robustness · 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 · 1Why humanoid reinforcement-learning pipelines report success that is not there: failures hidden in curriculum gates, evaluation scripts and reference motions as well as in the reward, with measured examples from one simulated humanoid · 1World Models · 1World models · 1World-Action Models · 1World-action model that retrains on its own successful, mediocre and failed real-robot rollouts and improves round after round · 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
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