1 citations · 1 across the 9 of their papers we have counts for
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Tele-rehabilitation with online skill transfer and adaptation in
Tianle Ni, Xiao Chen, Hamid Sadeghian +1
This paper proposes a tele-teaching framework for the domain of robot-assisted tele-rehabilitation. The system connects two robotic manipulators on therapist and patient side via b…
Learning a Shape-adaptive Assist-as-needed Rehabilitation Policy from Therapist-informed Input
Zhimin Hou, Jiacheng Hou, Xiao Chen +3
Therapist-in-the-loop robotic rehabilitation has shown great promise in enhancing rehabilitation outcomes by integrating the strengths of therapists and robotic systems. However, i…
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Zewen Yang, Xiaobing Dai, Dongfa Zhang +5
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize acros…
RCM Constraint-Consistent Dynamic Control in Surgical Robots
Yu Li, Hamid Sadeghian, Zewen Yang +2
Robotic-assisted minimally invasive surgery (RAMIS) requires accurate enforcement of the remote center of motion (RCM) constraint to ensure safe tool motion through a trocar. Exist…
Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
Zewen Yang, Dongfa Zhang, Xiaobing Dai +5
Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additional…
SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions
Xiaobing Dai, Zewen Yang, Dian Yu +4
Recent advances in generative modeling have led to promising results in robot motion planning, particularly through diffusion and flow matching (FM)-based models that capture compl…
SharedAssembly: A Data Collection Approach via Shared Tele-Assembly
Yansong Wu, Xiao Chen, Yu Chen +4
High-precision, tight-clearance assembly demonstrations are indispensable for training tactile-aware robotic foundation models, yet their acquisition is heavily bottlenecked by the…