Search Results for author: Jingxi Xu

Found 7 papers, 2 papers with code

Tactile-based Object Retrieval From Granular Media

no code implementations7 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.

Object Retrieval

TANDEM3D: Active Tactile Exploration for 3D Object Recognition

no code implementations19 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.

3D Object Recognition Decision Making +1

TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors

no code implementations1 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.

Decision Making Efficient Exploration +2

How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?

1 code implementation2 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.

Reinforcement Learning (RL)

Active Multitask Learning with Committees

no code implementations24 Mar 2021 Jingxi Xu, Da Tang, Tony Jebara

The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.

Transfer Learning

Learning a Decentralized Multi-arm Motion Planner

1 code implementation5 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.

Motion Planning Multi-agent Reinforcement Learning +2

Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation

no code implementations20 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.

Imitation Learning Navigate +3

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