4 citations · 7 across the 37 of their papers we have counts for
39 papers · 1 filter
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…
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…
Register-Routed Delayed Fusion: Rewiring Shortcut-Prone Observation Fusion in Visuomotor Imitation
Jieting Long, Weidong Cai, Weiming Zhi
Visuomotor imitation policies combine high-dimensional visual observations with compact signals such as proprioception, and their fusion topology determines when and through which…
SAKI: Skill Assembly and Kinematic Imitation from Human Videos for Long-Horizon Mobile Manipulation
Yijie Lu, James Zhao, Weiming Zhi
Learning from human videos offers a promising route to acquiring diverse manipulation skills. Extending this capability beyond tabletop settings to long-horizon mobile manipulation…
MAVP: Map-Aware Visuomotor Policies for Mobile Manipulation
Jinhe Tang, Ruixiao Dai, Weiming Zhi
Successful mobile manipulation requires coordinated base and arm motion while maintaining accurate spatial positioning. However, demonstration-trained policies can struggle to real…
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…
Learning In-Hand Object Reaching to General 6D Poses
Junxiao Lin, Tianyue Wu, Jie Yin +3
In-hand manipulation allows multi-fingered dexterous hands to reconfigure grasped objects without releasing and regrasping them. This improves manipulation efficiency by reducing r…
ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models
Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi
Contact-rich manipulation failures are often detected only after the robot has committed to contact. This is especially limiting in wrist-camera setups: close gripper--object views…
NestDex: Nested Policy Learning with Copilot Assisted Teleoperation for Dexterous Manipulation
James Zhao, Jinhe Tang, Mingyuan Ba +1
Dexterous manipulation promises substantially richer robot interaction with the physical world, but learning these behaviours remains constrained by the difficulty of collecting co…
Tri-Manual Visuomotor Imitation Learning of Robot Policies
James Zhao, Mingyuan Ba, Weiming Zhi
Bimanual teleoperation provides an effective way to collect robot demonstrations, but it assumes that the operator and robot have matching numbers of simultaneous control channels.…
AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies
Jinhe Tang, Weiming Zhi
Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single-step commands. Yet perception…
SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer
Xiaoxiang Dong, William Baron, Hongyi Chen +3
Transferring manipulation skills across object instances that share functionality but differ in geometry remains a fundamental challenge in robot learning. While recent corresponde…
BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling
Sreevardhan Sirigiri, Weiming Zhi, Fabio Ramos
Robots must generate trajectories that remain faithful to learned expert behavior while satisfying safety constraints and task-specific objectives specified only at inference time.…
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…
Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning
Gehan Zheng, Sanjay Seenivasan, Matthew Johnson-Roberson +1
Imitation learning has enabled robots to acquire complex visuomotor manipulation skills from demonstrations, but deployment failures remain a major obstacle, especially for long-ho…
SOFTMAP: Sim2Real Soft Robot Forward Modeling via Topological Mesh Alignment and Physics Prior
Ziyong Ma, Uksang Yoo, Jonathan Francis +3
While soft robot manipulators offer compelling advantages over rigid counterparts, including inherent compliance, safe human-robot interaction, and the ability to conform to comple…
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…
Cross-Modal Instructions for Robot Motion Generation
William Barron, Xiaoxiang Dong, Matthew Johnson-Roberson +1
Teaching robots novel behaviors typically requires motion demonstrations via teleoperation or kinaesthetic teaching, that is, physically guiding the robot. While recent work has ex…
Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations
Xiaoxiang Dong, Matthew Johnson-Roberson, Weiming Zhi
Learning from human video demonstrations offers a scalable alternative to teleoperation or kinesthetic teaching, but poses challenges for robot manipulators due to embodiment diffe…
Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation
Wei-Teng Chu, Tianyi Zhang, Matthew Johnson-Roberson +1
Implicit representations have been widely applied in robotics for obstacle avoidance and path planning. In this paper, we explore the problem of constructing an implicit distance r…
Bi-Manual Joint Camera Calibration and Scene Representation
Haozhan Tang, Tianyi Zhang, Matthew Johnson-Roberson +1
Robot manipulation, especially bimanual manipulation, often requires setting up multiple cameras on multiple robot manipulators. Before robot manipulators can generate motion or ev…