6 papers
Touch Where It Matters: Learning Local Tactile Policies for Fine-Grained Gripper Control
Mingyuan Ba, Yanan Zhou, James Zhao +1
Visual imitation policies can organise task motion, yet contact-rich object use demands gripper adjustments beyond coarse opening and closing. We study fine-grained gripper control…
Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot Manipulation
Zihao Li, Yanan Zhou, Ranpeng Qiu +2
In multi-robot manipulation, an action's value depends on how teammates respond. We present \method{} (Cooperative Action--Response Belief Estimation), a framework for learning \em…
StereoPatch: Patch-Aligned RGB-Depth Fusion for Spatial Perception in Robot Manipulation
Yanan Zhou, Zhaoyan Qian, James Zhao +1
Recent advances in robot imitation learning have produced visuomotor policies that predict actions directly from visual observations. Yet visually similar scenes can require differ…
PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation
Yanan Zhou, Ranpeng Qiu, Yincong Chen +2
Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation. However, deployment in open…
Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies
Yincong Chen, Ranpeng Qiu, Zihao Li +3
Collaborative mobile manipulation requires robots to coordinate with a partially observed partner while physically interacting through shared objects. This is difficult because fai…
TriPilot-FF: Coordinated Whole-Body Teleoperation with Force Feedback
Zihao Li, Yanan Zhou, Ranpeng Qiu +3
Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem: operators must coordinate a whe…