51 citations · 87 across the 22 of their papers we have counts for
23 papers
The Robot Data Factory
Sami Haddadin, Ivan Laptev, Ian Reid +18
Physical AI requires more than increasingly large robot datasets: intelligent robots acquire knowledge through continuous interaction with the physical world. We argue that the def…
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…
Video-to-BT: Generating Reactive Behavior Trees from Human Demonstration Videos for Robotic Assembly
Xiwei Zhao, Yiwei Wang, Yansong Wu +5
Modern manufacturing demands robotic assembly systems with enhanced flexibility and reliability. However, traditional approaches often rely on programming tailored to each product…
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…
APT*: Asymptotically Optimal Motion Planning via Adaptively Prolated Elliptical R-Nearest Neighbors
Liding Zhang, Sicheng Wang, Kuanqi Cai +5
Optimal path planning aims to determine a sequence of states from a start to a goal while accounting for planning objectives. Popular methods often integrate fixed batch sizes and…
Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization
Liding Zhang, Yao Ling, Zhenshan Bing +3
Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continu…
Elliptical K-Nearest Neighbors -- Path Optimization via Coulomb's Law and Invalid Vertices in C-space Obstacles
Liding Zhang, Zhenshan Bing, Yu Zhang +5
Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction…
Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
Liding Zhang, Kejia Chen, Kuanqi Cai +7
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Info…
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…
Motion Planning for Robotics: A Review for Sampling-based Planners
Liding Zhang, Kuanqi Cai, Zewei Sun +5
Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion pla…
Behavior Tree Generation using Large Language Models for Sequential Manipulation Planning with Human Instructions and Feedback
Jicong Ao, Yansong Wu, Fan Wu +1
In this work, we propose an LLM-based BT generation framework to leverage the strengths of both for sequential manipulation planning. To enable human-robot collaborative task plann…
LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks
Kejia Chen, Zheng Shen, Yue Zhang +5
Large Language Models (LLMs) have gained popularity in task planning for long-horizon manipulation tasks. To enhance the validity of LLM-generated plans, visual demonstrations and…
TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
Yansong Wu, Zongxie Chen, Fan Wu +6
Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precisi…
LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning
Jicong Ao, Fan Wu, Yansong Wu +2
Robotic assembly tasks remain an open challenge due to their long horizon nature and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for t…
Visuo-Tactile Exploration of Unknown Rigid 3D Curvatures by Vision-Augmented Unified Force-Impedance Control
Kübra Karacan, Anran Zhang, Hamid Sadeghian +2
Despite recent advancements in torque-controlled tactile robots, integrating them into manufacturing settings remains challenging, particularly in complex environments. Simplifying…
Tactile-Morph Skills: Energy-Based Control Meets Data-Driven Learning
Anran Zhang, Kübra Karacan, Hamid Sadeghian +3
Robotic manipulation is essential for modernizing factories and automating industrial tasks like polishing, which require advanced tactile abilities. These robots must be easily se…
Real-time Contact State Estimation in Shape Control of Deformable Linear Objects under Small Environmental Constraints
Kejia Chen, Zhenshan Bing, Yansong Wu +4
Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust…
Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning
Liding Zhang, Zhenshan Bing, Kejia Chen +8
In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where p…
Contact-aware Shaping and Maintenance of Deformable Linear Objects With Fixtures
Kejia Chen, Zhenshan Bing, Fan Wu +4
Studying the manipulation of deformable linear objects has significant practical applications in industry, including car manufacturing, textile production, and electronics automati…