MEKAWIRE
Research

Papers

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

4 papers
TopicAllManipulation · 56VLA · 33Navigation · 24Robotics · 20Learning · 19Perception · 17Humanoid · 14Locomotion · 8Safety · 8Simulation · 8World Action Models · 4Aerial manipulation · 2Dexterous manipulation · 2Sim-to-Real Manipulation · 2Social Navigation · 2Vision-Language Navigation · 2World models · 2World-action model · 23D Assembly · 13DGS dataset · 15G Simulation · 1AMR Operations · 1Active vision manipulation · 1Adaptive MPC · 1Adaptive action horizons · 1Adaptive intervention · 1Aerial · 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 · 1Continuum sensing · 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 · 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 control · 1Humanoid loco-manipulation · 1Humanoid safety · 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 · 1Policy Optimization · 1Preference Learning · 1Quadruped locomotion · 1Real-time VLA · 1Real-to-sim · 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 · 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-Action Models · 1
LevelAnyBeginnerIntermediateExpert
Filtered by technology reinforcement-learning · clear
arXiv
Adaptive interventionIntermediate

UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning

Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan, Zaijia Yang, Yuanjiang Xue, Ziwei Wang

UniIntervene++ is a preprint describing an online learned intervention agent that selects among the robot's own RL policy, trajectory correction, and a task-structured CodePolicy during real-robot reinforcement learning. Across five manipulation tasks on a UR7e arm it reaches 89.67% average success, 6 points above the best baseline, while cutting human-controlled steps to 0.77%. This could reduce the skilled-operator burden needed to make online robot learning practical.

RL
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arXiv
Curriculum RLIntermediate

Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning

Juri Pfammatter, Kaixian Qu, Clemens Schwarke, Victor Klemm, Marco Hutter

This preprint introduces BVER, a bidirectional curriculum that grows start states from the goal and goals from the initial state distribution, biased toward unexplored task space and steered together RRT-style. On quadruped box climbing it reaches 95% success on a 0.4 m box in about 65% fewer iterations than the best reference-free baseline and is the only reference-free method tested to climb a 0.7 m box. It matters because it removes the need for demonstrations or hand-designed rewards in sparse-reward robot learning.

RL
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arXiv
Quadruped locomotionIntermediate

PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger

In this preprint, PROMO conditions a single quadruped locomotion policy on a three-way semantic preference over tracking, stability, and energy efficiency. On a Unitree Go2, changing only the preference reduces specific energy by up to 30.4%, position error by 38.7%, and peak body-attitude deviation by 59.0% relative to a balanced setting. This gives legged robots an operator-facing trade-off dial without retraining or switching controllers.

RL
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arXiv
Endoscopic navigationIntermediate

ColoACT: Multi-Cue Action Chunking for Smooth Autonomous Colon Navigation on a Self-Propelled Endoscopic Robot

Jian Hu, Shujing He, Leixin Chang, Zongze Li, Ding Huang, Chaoyang Shi, Chengzhi Hu

This is a preprint describing ColoACT, an imitation-learning navigation system for a compact self-propelled endoscopic robot. The policy fuses RGB, relative depth, and a gradient-based pseudo-elevation map with an Action Chunking Transformer and temporal ensembling. In ex-vivo porcine colons it reached 85.4% success on straight and 72.5% on curved segments, with smooth steering and success on 90-degree turns (70%) and double bends (60%), suggesting a path toward reducing physician burden in colonoscopy.

RL
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Topic pages

Manipulation · 56VLA · 32Navigation · 24General robotics · 20Robot learning · 19Perception · 17Humanoid · 14Safety · 8Simulation · 8Locomotion · 7World Action Models · 5Aerial 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.