4 papers
AISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior
Zhiming Chen, Linfang Zheng, Kun Zhang +4
Reliable depth perception is critical for robotic manipulation, especially for non-Lambertian objects such as transparent or highly specular surfaces, where raw depth measurements…
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator
Kaiwen Jiang, Zhen Fu, Junde Guo +2
In this paper, we study the whole-body loco-manipulation problem using reinforcement learning (RL). Specifically, we focus on the problem of how to coordinate the floating base and…
A Practical Introduction to Deep Reinforcement Learning
Yinghan Sun, Hongxi Wang, Hua Chen +1
Deep reinforcement learning (DRL) has emerged as a powerful framework for solving sequential decision-making problems, achieving remarkable success in a wide range of applications,…
Generative Artificial Intelligence in Robotic Manipulation: A Survey
Kun Zhang, Peng Yun, Jun Cen +11
This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulati…