no code implementations • 7 Feb 2024 • Jingxi Xu, Yinsen Jia, Dongxiao Yang, Patrick Meng, Xinyue Zhu, Zihan Guo, Shuran Song, Matei Ciocarlie
We also introduce a training curriculum that enables learning these behaviors in simulation, followed by zero-shot transfer to real hardware.
no code implementations • 19 Sep 2022 • Jingxi Xu, Han Lin, Shuran Song, Matei Ciocarlie
In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals.
no code implementations • 1 Mar 2022 • Jingxi Xu, Shuran Song, Matei Ciocarlie
Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based object interaction.
1 code implementation • 2 Apr 2021 • Jingxi Xu, Bruce Lee, Nikolai Matni, Dinesh Jayaraman
The difficulty of optimal control problems has classically been characterized in terms of system properties such as minimum eigenvalues of controllability/observability gramians.
no code implementations • 24 Mar 2021 • Jingxi Xu, Da Tang, Tony Jebara
The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.
1 code implementation • 5 Nov 2020 • Huy Ha, Jingxi Xu, Shuran Song
In this paper, we tackle this problem with multi-agent reinforcement learning, where a decentralized policy is trained to control one robot arm in the multi-arm system to reach its target end-effector pose given observations of its workspace state and target end-effector pose.
no code implementations • 20 Sep 2019 • David Watkins-Valls, Jingxi Xu, Nicholas Waytowich, Peter Allen
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments.