no code implementations • 4 Feb 2024 • Lirui Wang, Jialiang Zhao, Yilun Du, Edward H. Adelson, Russ Tedrake
Training general robotic policies from heterogeneous data for different tasks is a significant challenge.
1 code implementation • 2 Oct 2023 • Lirui Wang, Yiyang Ling, Zhecheng Yuan, Mohit Shridhar, Chen Bao, Yuzhe Qin, Bailin Wang, Huazhe Xu, Xiaolong Wang
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data.
1 code implementation • 2 Oct 2023 • Lirui Wang, Kaiqing Zhang, Allan Zhou, Max Simchowitz, Russ Tedrake
We show that FLEET-MERGE consolidates the behavior of policies trained on 50 tasks in the Meta-World environment, with good performance on nearly all training tasks at test time.
1 code implementation • ICCV 2023 • Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks.
Ranked #1 on on YCB-Video
no code implementations • CVPR 2023 • Allan Zhou, Moo Jin Kim, Lirui Wang, Pete Florence, Chelsea Finn
Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors.
1 code implementation • 20 Oct 2022 • Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake
Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems.
no code implementations • 23 Sep 2021 • Alexey Kamenev, Lirui Wang, Ollin Boer Bohan, Ishwar Kulkarni, Bilal Kartal, Artem Molchanov, Stan Birchfield, David Nistér, Nikolai Smolyanskiy
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving.
1 code implementation • 2 Oct 2020 • Lirui Wang, Yu Xiang, Wei Yang, Arsalan Mousavian, Dieter Fox
We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects.
1 code implementation • 22 Nov 2019 • Lirui Wang, Yu Xiang, Dieter Fox
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem.
Robotics