69 citations · 108 across the 10 of their papers we have counts for
10 papers
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari +10
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper…
Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly
Yunsheng Tian, Jie Xu, Yichen Li +5
Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, p…
Learning Dense Reward with Temporal Variant Self-Supervision
Yuning Wu, Jieliang Luo, Hui Li
Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, s…
Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation
Kai-Hung Chang, Chin-Yi Cheng, Jieliang Luo +3
Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the progr…
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang +3
The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have larg…
A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly
Jieliang Luo, Hui Li
In this work we propose a learning approach to high-precision robotic assembly problems. We focus on the contact-rich phase, where the assembly pieces are in close contact with eac…
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
Karl D. D. Willis, Yewen Pu, Jieliang Luo +5
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despi…
Dynamic Experience Replay
Jieliang Luo, Hui Li
We present a novel technique called Dynamic Experience Replay (DER) that allows Reinforcement Learning (RL) algorithms to use experience replay samples not only from human demonstr…
Visual Diagnostics for Deep Reinforcement Learning Policy Development
Jieliang Luo, Sam Green, Peter Feghali +2
Modern vision-based reinforcement learning techniques often use convolutional neural networks (CNN) as universal function approximators to choose which action to take for a given v…
A Learning Framework for High Precision Industrial Assembly
Yongxiang Fan, Jieliang Luo, Masayoshi Tomizuka
Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic asse…