2 citations · 3 across the 11 of their papers we have counts for
13 papers
ACTS: Learning Visuo-Tactile World Models from Human Physical Interaction
Yuxiang Ma, Weihan Xu, Pengfei Ye +5
Abstract Action-conditioned world models for contact-rich manipulation require data that captures how contact forms, slides, and releases under varying force. Such data are slow to…
Robot Learning with Visual Predicted Force
Haonan Chen, Feiyang Wu, Yuxiang Ma +11
Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through visual force prediction fro…
A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution
Guangming Wang, Pengfei Ye, Qizhen Ying +7
Robots that follow open-ended language instructions need to connect semantic intent to visual scene understanding, geometric feasibility, object states, and physical interaction co…
RoboEdit: Turning Human Manipulation Videos into Scalable Robot Experience
Yaowei Guo, Zeng Tao, Yuxin Jiang +7
Collecting robot hand-object interaction data is costly and embodiment-specific, yet abundant human-object videos remain unusable for robot training. We present RoboEdit, a human-t…
You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact
Pengfei Ye, Yuxiang Ma, Haonan Chen +5
Accurate 6-DoF object pose estimation is fundamental to robotic manipulation, yet vision-based methods often fail under occlusion, poor lighting, and reflective or transparent surf…
CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation
Haonan Chen, Yuxiang Ma, Stephen Tian +7
Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-box generalization to new tasks.…
InvariantCloud: A Globally Invariant, Uniquely Indexed Point Cloud Framework for Robust 6-DoF Tactile Pose Tracking
Pengfei Ye, Yuxiang Ma, Yi Zhou +3
Recent advances in imitation learning and vision-language models highlight the need for high-fidelity tactile perception, with 6-DoF tactile object pose estimation providing a cruc…
RichMap: A Reachability Map Balancing Precision, Efficiency, and Flexibility for Rich Robot Manipulation Tasks
Yupu Lu, Yuxiang Ma, Jia Pan
This paper presents RichMap, a high-precision reachability map representation designed to balance efficiency and flexibility for versatile robot manipulation tasks. By refining the…
Object Recognition and Force Estimation with the GelSight Baby Fin Ray
Sandra Q. Liu, Yuxiang Ma, Edward H. Adelson
Recent advances in soft robotic hands and tactile sensing have enabled both to perform an increasing number of complex tasks with the aid of machine learning. In particular, we pre…
Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks
Jialiang Zhao, Yuxiang Ma, Lirui Wang +1
This paper presents T3: Transferable Tactile Transformers, a framework for tactile representation learning that scales across multi-sensors and multi-tasks. T3 is designed to overc…
GelLink: A Compact Multi-phalanx Finger with Vision-based Tactile Sensing and Proprioception
Yuxiang Ma, Jialiang Zhao, Edward Adelson
Compared to fully-actuated robotic end-effectors, underactuated ones are generally more adaptive, robust, and cost-effective. However, state estimation for underactuated hands is u…
Scalable, Simulation-Guided Compliant Tactile Finger Design
Yuxiang Ma, Arpit Agarwal, Sandra Q. Liu +2
Compliant grippers enable robots to work with humans in unstructured environments. In general, these grippers can improve with tactile sensing to estimate the state of objects arou…
GelSight Baby Fin Ray: A Compact, Compliant, Flexible Finger with High-Resolution Tactile Sensing
Sandra Q. Liu, Yuxiang Ma, Edward H. Adelson
The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to…