7 papers
Robot Self-Improvement via Human-Video Dynamics Models
Hanzhi Chen, Anran Zhang, Simon Schaefer +5
A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, and the experience of failure.…
Just in time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning
Kuanqi Cai, Liding Zhang, Xinwen Su +6
In high-dimensional robotic path planning, traditional sampling-based methods often struggle to efficiently identify both feasible and optimal paths in complex, multi-obstacle envi…
TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Tasks
Tailai Cheng, Kejia Chen, Lingyun Chen +8
Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on…
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…
Multi-Robot Assembly of Deformable Linear Objects Using Multi-Modal Perception
Kejia Chen, Celina Dettmering, Florian Pachler +7
Industrial assembly of deformable linear objects (DLOs) such as cables offers great potential for many industries. However, DLOs pose several challenges for robot-based automation…
Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning
Yuan Meng, Xiangtong Yao, Kejia Chen +4
Reinforcement learning (RL) methods typically learn new tasks from scratch, often disregarding prior knowledge that could accelerate the learning process. While some methods incorp…