ロボティクス研究論文 — 21ページ

WARP-RM: Self-Supervised Robot Task Progress Estimation with Video Time-Warping

Justin Yu, Andrew Goldberg, Kavish Kondap, Karim El-Refai +6

DexCompose Lets One Robot Hand Chain Dexterous Skills Without Retraining

DexCompose composes dexterous manipulation policies on a single hand without retraining, achieving over 90% success using finger-aware masking and dual residual stabilizers.

Dihong Huang, Zhenyu Wei, Zhuxiu Xu +3

Robotics6月28日

LA4VLA: Teaching Robots to Follow Instructions Without Seeing

Recent vision-language-action (VLA) models have a hidden weakness: they often rely on visual cues rather than truly understanding language instructions. A ne…

Tao Lin, Yuxin Du, Yiran Mao +13

Robotics6月28日

OctoSense: Self-Supervised Learning Unifies 8 Sensors for Robust Robot Perception

Anthony Bisulco, Jeremy Wang, Kostas Daniilidis +2

AI6月28日

Hallucination in World Models is Predictable and Preventable

Nicklas Hansen, Xiaolong Wang

Robotics6月27日

VibeAct: Using Vibration to Enable Reactive Robot Hand Dexterity

Open-world manipulation requires rapid responses to contact and slip events. When a fingertip first makes contact, or when a grasp transitions from sticking …

Yuemin Mao, Uksang Yoo, Jean Oh +2

Robotics6月27日

New Reinforcement Learning Method Lets Drones Race Fast in Unseen Tracks Without Retraining

Researchers show reinforcement learning for drone racing can achieve zero-shot generalization without sacrificing speed.

Jonathan Green, Jiaxu Xing, Nico Messikommer +2

Robotics6月27日

Continual Robot Learning: Adapting to Changing Dynamics in Real Time

Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4

Robotics6月26日

RouterVLA: Using Routine Smoke Tests to Pick the Best Robot VLA Model

Xingyu Ren, Chugang Yi, Ge Ma +1

Robotics6月26日

World Action Models Use Generative Replays to Prevent Robot Forgetting

We presented Recurrent Generative Replay (ReGen), the first continual learning framework to leverage the generative capabilities of World Action Models (WAMs…

Manish Kumar Govind, Dominick Reilly, Smit Patel +2

Robotics6月26日

Massive Open Dataset and Toolkit for Robot Behavior Cloning

ABC stack releases 130K-episode teleoperation dataset and open-source toolkit for scalable robot behavior cloning.

Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh +15

Robotics6月25日

Deep Reinforcement Learning Makes Cable-Driven Soft Robot Arms More Computationally Efficient

Cheng Ouyang, Moeen Ul Islam, Kaixiang Zhang +3

Robotics6月25日

ForceBand: Learning Forceful Robot Manipulation Through Muscle Sensing

Botao He, Zhi Wang, Linna Kuang +8

AI6月25日

Robots Learn the Feel of Movement Before Learning to See and Act

A two-stage training framework introduces a dedicated action prior learning stage before vision-language-action (VLA) training. By training a flow-matching-b…

Dong Jing, Tianqi Zhang, Jiaqi Liu +5

Robotics6月24日

OpenReLoc: Object-Level Camera Relocalization with Open-Vocabulary Understanding

OpenReLoc uses open-vocabulary object matching and LLM descriptions to robustly estimate camera pose from a single image, outperforming closed-vocabulary methods on real indoor scenes.

Zhaopeng Cui, Jiarui Hu, Jingbo Liu +7

Warehouse6月24日

Vision-Language Model Gives Warehouse Robots Context-Aware Semantic Maps

Marvin Rüdt, Hao Pang, Constantin Enke +2

AI6月24日

InSight: How Robots Learn New Skills Without Human Help

Maggie Wang, Lars Osterberg, Stephen Tian +3

Robotics6月23日

Robots Learn Physical Reasoning from Scalable Human Hand Data

Researchers have developed LaST-HD, a framework that lets robots learn complex manipulation skills by observing human hand demonstrations. By aligning human …

Jiaming Liu, Yinxi Wang, Chenyang Gu +15

Robotics6月23日

LIBERO-Safety Benchmark Puts Vision-Language-Action Robots Through Physical and Semantic Safety Tests

Researchers introduced LIBERO-Safety, a comprehensive benchmark that systematically evaluates how vision-language-action (VLA) models handle physical safety …

Rongxu Cui, Zongzheng Zhang, Jingrui Pang +11

AI6月23日

AutoDex: Fully Automated Dexterous Grasping Data Collection at 75+ Trials Per Hour

Mingi Choi, Gunhee Kim, Jisoo Kim +4

研究Papers

WARP-RM: Self-Supervised Robot Task Progress Estimation with Video Time-Warping

DexCompose Lets One Robot Hand Chain Dexterous Skills Without Retraining

LA4VLA: Teaching Robots to Follow Instructions Without Seeing

OctoSense: Self-Supervised Learning Unifies 8 Sensors for Robust Robot Perception

Hallucination in World Models is Predictable and Preventable

VibeAct: Using Vibration to Enable Reactive Robot Hand Dexterity

New Reinforcement Learning Method Lets Drones Race Fast in Unseen Tracks Without Retraining

Continual Robot Learning: Adapting to Changing Dynamics in Real Time

RouterVLA: Using Routine Smoke Tests to Pick the Best Robot VLA Model

World Action Models Use Generative Replays to Prevent Robot Forgetting

Massive Open Dataset and Toolkit for Robot Behavior Cloning

Deep Reinforcement Learning Makes Cable-Driven Soft Robot Arms More Computationally Efficient

ForceBand: Learning Forceful Robot Manipulation Through Muscle Sensing

Robots Learn the Feel of Movement Before Learning to See and Act

OpenReLoc: Object-Level Camera Relocalization with Open-Vocabulary Understanding

Vision-Language Model Gives Warehouse Robots Context-Aware Semantic Maps

InSight: How Robots Learn New Skills Without Human Help

Robots Learn Physical Reasoning from Scalable Human Hand Data

LIBERO-Safety Benchmark Puts Vision-Language-Action Robots Through Physical and Semantic Safety Tests

AutoDex: Fully Automated Dexterous Grasping Data Collection at 75+ Trials Per Hour