6 papers
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…
CoRE: Learning Collaboration-Role Experts for Decentralized Collaborative Manipulation with One Policy
Yanan Zhou, Zhaoyan Qian, Zihao Li +3
Collaborative manipulation requires robots to perform complementary actions as interactions unfold. We study single-policy decentralized collaboration: every robot runs the same po…
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…
TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation
Zihao Li, Ranpeng Qiu, Yincong Chen +2
Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study delayed-evidence tasks, where an early cue disappears before a later d…
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…