10 papers
Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation
Teng Xue, Alberto Rigo, Bingjian Huang +4
Contact-rich manipulation is central to many everyday human activities, requiring continuous adaptation to contact uncertainty and external disturbances through multi-modal percept…
Monte Carlo Tree Search with Tensor Factorization for Optimization Problems in Robotics
Teng Xue, Yan Zhang, Amirreza Razmjoo +1
Many robotic tasks, such as inverse kinematics, motion planning, and contact-rich manipulation, can be formulated as optimization problems. Solving these problems requires addressi…
Efficient and Real-Time Motion Planning for Robotics Using Projection-Based Optimization
Xuemin Chi, Hakan Girgin, Tobias Löw +6
Generating motions for robots interacting with objects of various shapes is a complex challenge, further complicated by the robot geometry and multiple desired behaviors. While cur…
Sampling-Based Constrained Motion Planning with Products of Experts
Amirreza Razmjoo, Teng Xue, Suhan Shetty +1
We present a novel approach to enhance the performance of sampling-based Model Predictive Control (MPC) in constrained optimization by leveraging products of experts. Our methodolo…
Robust Contact-rich Manipulation through Implicit Motor Adaptation
Teng Xue, Amirreza Razmjoo, Suhan Shetty +1
Contact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control.…
Robust Manipulation Primitive Learning via Domain Contraction
Teng Xue, Amirreza Razmjoo, Suhan Shetty +1
Contact-rich manipulation plays an important role in human daily activities, but uncertain parameters pose significant challenges for robots to achieve comparable performance throu…
Learn2Decompose: Learning Problem Decomposition for Efficient Sequential Multi-object Manipulation Planning
Yan Zhang, Teng Xue, Amirreza Razmjoo +1
We present an efficient task and motion replanning approach for sequential multi-object manipulation in dynamic environments. Conventional Task And Motion Planning (TAMP) solvers e…
Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning
Teng Xue, Amirreza Razmjoo, Suhan Shetty +1
Recent advances in robot skill learning have unlocked the potential to construct task-agnostic skill libraries, facilitating the seamless sequencing of multiple simple manipulation…
Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments
Yan Zhang, Teng Xue, Amirreza Razmjoo +1
Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitati…
D-LGP: Dynamic Logic-Geometric Program for Reactive Task and Motion Planning
Teng Xue, Amirreza Razmjoo, Sylvain Calinon
Many real-world sequential manipulation tasks involve a combination of discrete symbolic search and continuous motion planning, collectively known as combined task and motion plann…