5 papers
WoVR+: World Model as Reliable Simulator for Versatile VLA Policy Optimization with RL
Shangqing Zhou, Zhennan Jiang, Ning Ma +9
Action-conditioned world models can reduce costly real-robot interaction in reinforcement learning for Vision-Language-Action models (VLA-RL), but hallucinations can mislead policy…
TRACT: Temporally Routed Action Chunks with Chronological Phase Authority for Contact-Rich Manipulation
Jiahao Liu, Kento Kawaharazuka, Tasuku Makabe +1
Action chunking shortens the effective decision horizon of robot imitation learning by predicting multiple future actions, while conventional phase conditioning describes the curre…
MDIR: A Task-Manifold Impedance Retargeting Method for Contact-Rich Teleoperation
Liu Jiahao, Kento Kawaharazuka, Tasuku Makabe +1
Fixed Cartesian impedance makes contact-rich teleoperation demonstrations practical, but gains that secure progress and contact support also determine impact and force variability.…
WALA Learning Executable Latent Actions from Action-Labeled Demonstrations and Action-Free Videos
Jiahao Liu, Zhongpu Xia, Yupeng Zheng +14
Human videos provide rich information about object manipulation at a scale difficult to reproduce with robots, but often lack action annotations that can directly supervise robot p…
InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
Jiahao Liu, Cui Wenbo, Zhongpu Xia +3
Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipulator and robust perception unde…