6 papers
Hybrid TD3: Overestimation Bias Analysis and Stable Policy Optimization for Hybrid Action Space
Thanh-Tuan Tran, Thanh Nguyen Canh, Nak Young Chong +1
Reinforcement learning in discrete-continuous hybrid action spaces presents fundamental challenges for robotic manipulation, where high-level task decisions and low-level joint-spa…
Multimodal Adversarial Quality Policy for Safe Grasping
Kunlin Xie, Chenghao Li, Haolan Zhang +1
Vision-guided robot grasping based on Deep Neural Networks (DNNs) generalizes well but poses safety risks in the Human-Robot Interaction (HRI). Recent works solved it by designing…
Online 3D Bin Packing with Fast Stability Validation and Stable Rearrangement Planning
Ziyan Gao, Lijun Wang, Yuntao Kong +1
The Online Bin Packing Problem (OBPP) is a sequential decision-making task in which each item must be placed immediately upon arrival, with no knowledge of future arrivals. Althoug…
Quality-focused Active Adversarial Policy for Safe Grasping in Human-Robot Interaction
Chenghao Li, Razvan Beuran, Nak Young Chong
Vision-guided robot grasping methods based on Deep Neural Networks (DNNs) have achieved remarkable success in handling unknown objects, attributable to their powerful generalizabil…
Pyramid-Monozone Synergistic Grasping Policy in Dense Clutter
Chenghao Li, Nak Young Chong
Grasping a diverse range of novel objects in dense clutter poses a great challenge to robotic automation mainly due to the occlusion problem. In this work, we propose the Pyramid-M…
An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement
Peiwen Zhou, Ziyan Gao, Chenghao Li +1
This paper presents an efficient deep reinforcement learning (DRL) framework for online 3D bin packing (3D-BPP). The 3D-BPP is an NP-hard problem significant in logistics, warehous…