6 papers · 1 filter
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…