Tao Chen (陈涛)

I am a Ph.D. student in EECS at MIT CSAIL, advised by Prof. Pulkit Agrawal. My research interests revolve around the intersection of robot learning, manipulation, and navigation.

I received my master's degree from the Robotics Institute, Carnegie Mellon University (CMU RI) in May 2019, advised by Prof. Abhinav Gupta.

Prior to this, I was a research engineer in Shanghai LX Robotics, where I conducted research on object detection, image segmentation, deep reinforcement learning in robotics, SLAM, etc.

I earned my bachelor's degree from Shanghai Jiao Tong University (SJTU) in June 2016, majoring in mechanical engineering and automation. I was also an exchange student (GEARE program) at School of Mechanical Engineering, Purdue University.

Email  /  CV  /  Google Scholar  /  GitHub /  LinkedIn


  • Our paper on in-hand object reorientation wins the Best Paper Award at CoRL 2021.
  • Our paper on in-hand object reorientation is accepted to CoRL 2021 (oral).
  • Our paper on dynamic vision-aware locomotion is accepted to CoRL 2021.
  • Our paper on end-to-end differentiable simulator for robot design and control co-optimization is accepted to RSS 2021.
  • Our paper on data-efficient policy learning for microrobots with complex dynamics is accepted to ICRA 2021.
  • New course: Computational Sensorimotor Learning is online, checkout the lecture videos on YouTube.
  • A crash course on deep learning and robotics: Deep Learning for Control. The lecture videos are available online.
  • Our python-oriented robot learning library, AIRobot, is open-sourced!
  • Our open-source robotics research platform, PyRobot, is now online!

sym A System for General In-Hand Object Re-Orientation
Tao Chen, Jie Xu, Pulkit Agrawal
Conference on Robot Learning (CoRL), 2021 (Best Paper Award), (Oral, acceptance rate: 6.5%)
paper / arXiv / bibtex / project page / oral talk @article{chen2021system,
title={A System for General In-Hand Object Re-Orientation},
author={Chen, Tao and Xu, Jie and Agrawal, Pulkit},
journal={Conference on Robot Learning},

Press coverage: MIT News, MIT CSAIL News, AZO Robotics, AIHub, AI科技评论, Tech Xplore, Communications of the ACM, Inceptive Mind, IEEE Spectrum, The Hack Posts, Tectales, The Robot Report

A system for general in-hand object reorientation.

sym Learning to Jump from Pixels
Gabriel Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal
Conference on Robot Learning (CoRL), 2021
paper / bibtex / project page @article{margolis2021jumping,
title={Learning to Jump from Pixels},
author={Margolis, Gabriel and Chen, Tao and Paigwar, Kartik and Fu, Xiang and Kim, Donghyun and Kim, Sangbae and Agrawal, Pulkit},
journal={Conference on Robot Learning},

Press Coverage: MIT News, AZO Robotics, The Robot Report

A hierarchical control framework for dynamic vision-aware locomotion.

sym Topological Experience Replay for Fast Q-Learning
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal
ICML workshop on Reinforcement Learning for Real Life , 2021
paper / bibtex / video / @article{hong2021ter,
title={Topological Experience Replay for Fast $Q$-Learning},
author={Hong, Zhang-Wei and Chen, Tao and Lin, Yen-Chen and Pajarinen, Joni and Agrawal, Pulkit},
journal={International Conference on Machine Learning Workshop (Reinforcement Learning for Real Life)},

A fast Q-learning method by building a topological graph in the replay buffer.

sym An End-to-End Differentiable Framework for Contact-Aware Robot Design
Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik, Shinjiro Sueda, Pulkit Agrawal
Robotics: Science and Systems (RSS) , 2021
paper / arXiv / bibtex / project page / code / video / talk @article{xu2021diffsim,
title={An End-to-End Differentiable Framework for Contact-Aware Robot Design},
author={Xu, Jie and Chen, Tao and Zlokapa, Lara and Matusik, Wojciech and Sueda, Shinjiro and Agrawal, Pulkit},
journal={Robotics: Science and Systems},

Press Coverage: MIT News, Tectales

Computational method for design task-specific robotic hands.

sym Residual Model Learning for Microrobot Control
Joshua Gruenstein, Tao Chen, Neel Doshi, Pulkit Agrawal
IEEE International Conference on Robotics and Automation (ICRA) , 2021
paper / bibtex / project page / video @article{gruenstein2021residual,
title={Residual Model Learning for Microrobot Control},
author={Gruenstein, Joshua and Chen, Tao and Doshi, Neel and Agrawal, Pulkit},
journal={International Conference on Robotics and Automation},

A data-efficient learning method for controlling microrobots with complex dynamics.

sym Language Inference for Reward Learning
Xiang Fu, Tao Chen, Pulkit Agrawal, Tommi S. Jaakkola
NeurIPS Biological and Artifical RL workshop, 2020
paper / bibtex @inproceedings{fu2020language,
author = {Xiang Fu and Tao Chen and Pulkit Agrawal and Tommi Jaakkola},
title = {Language Inference for Reward Learning},
booktitle = {Advances in Neural Information Processing Systems Workshop (Biological and Artificial Reinforcement Learning)},
year = {2020}

Reward learning by using formal language (regular expression) to capture the reward structure.

sym Learning to Learn from Failures using Replay
Tao Chen, Pulkit Agrawal
ICML BIG workshop, 2020
paper(workshop version) / bibtex / project page @inproceedings{chen2020memory,
author = {Tao Chen and Pulkit Agrawal},
title = {Learning to Learn from Failures using Replay},
booktitle = {International Conference on Machine Learning Workshop (Inductive Biases, Invariances and Generalization in RL)},
year = {2020}

Remembering failures aids faster learning by preventing the agent to oscillate between mistakes.

PyRobot: An Open-source Robotics Framework for Research and Benchmarking
Adithya Murali*, Tao Chen*, Kalyan Vasudev Alwala*, Dhiraj Gandhi*, Lerrel Pinto, Saurabh Gupta, Abhinav Gupta [* Equal contribution]
paper / bibtex / project page / code GitHub Star / facebook AI blog @article{pyrobot2019,
author = {Adithyavairavan Murali* and Tao Chen* and Kalyan Vasudev Alwala* and Dhiraj Gandhi* and Lerrel Pinto and Saurabh Gupta and Abhinav Gupta},
title = {{PyRobot}: An Open-source Robotics Framework for Research and Benchmarking},
journal = {CoRR},
volume = {abs/1906.08236},
year = {2019},
url = {https://arxiv.org/abs/1906.08236},
archivePrefix = {arXiv},
eprint = {1906.08236}

Press Coverage: WIRED, VentureBeat, THE ROBOT REPORT, SiliconANGLE, IB Times, SD Times, Medium

An easy-to-use python interface for robot learning and a low-cost robot learning platform.

Learning Exploration Policies for Navigation
Tao Chen, Saurabh Gupta, Abhinav Gupta
International Conference on Learning Representations (ICLR), 2019
paper / bibtex / project page / video / code / poster @inproceedings{chen2018learning,
author = "Chen, Tao and Gupta, Saurabh and Gupta, Abhinav",
title = "Learning Exploration Policies for Navigation",
booktitle = "International Conference on Learning Representations",
year = "2019",
url = "https://openreview.net/forum?id=SyMWn05F7"

A framework for learning to explore novel environments with on-board sensors in the testing time.

Hardware Conditioned Policies for Multi-Robot Transfer Learning
Tao Chen, Adithya Murali, Abhinav Gupta
Advances in Neural Information Processing Systems (NeurIPS), 2018
paper / bibtex / project page / video / code / poster @inproceedings{chen2018hardware,
title={Hardware Conditioned Policies for Multi-Robot Transfer Learning},
author={Chen, Tao and Murali, Adithyavairavan and Gupta, Abhinav},
booktitle={Advances in Neural Information Processing Systems},

One policy to control many robots that are kinematically and dynamically different.

Development of a Soft Elastomeric Gripper for Dexterous Grasping
Tao Chen, Guo-Ying Gu
Bachelor's Thesis, 2016
Awarded 2016 Excellent Bachelor Thesis (Top 1%) of Shanghai Jiao Tong University
project page

A four-fingered soft gripper with multi-cavity pneumatic elastomer actuators (MCPEA) for grasping objects with different sizes, shapes, fragility.

Work Experience

Autonomous Exploration for Dense Map Construction
Tao Chen
Shanghai LX Robotics, 2017

A key step for robots to get popularized into our daily life is that robots should be able to automatically explore the new environment when they are deployed in new houses or buildings. In this project, I combine the strength of motion planning (OMPL and SBPL), frontier-based exploration, SLAM (ORB-SLAM2), and object recognition and segmentation (FCIS) techniques to build an automatic mapping system that can autonomously explore the new houses, recognize daily objects and remember their locations while keep building the dense map as it moves. After the map is built, the robot can be asked to find and move to the objects it has seen (like cup, monitor) autonomously.


MIT 6.884 Computational Sensorimotor Learning - Spring 2021
Teaching Assistant (TA)

MIT 6.S090 Deep Learning for Control - IAP 2021

CMU 16-824 Visual Learning and Recognition - Spring 2019
Teaching Assistant (TA)

Course Projects

Robot Construction via Planning and Learning
Tao Chen, Xianyi Cheng
Learning for Manipulation, 2018
Instructor: Oliver Kroemer
video1 / video2 / video3 / code / report

We combined the symbolic planning and supervised learning to efficiently learn to move a set of blocks from an initial configuration to a goal configuration (a.k.a, robot construction problem). The symbolic planning module plans the sequence actions (path) to move the blocks (a block or a sub-assembly) to reach the goal configurations. The supervised learning module (stability checker) predicts whether the state (RGB image) is stable or not so that the planning module only plans with the actions that lead to stable states. We used domain randomization techniques to generate more diversified visual data to make the stability checker more robust. These two modules combined lead to an effective way to solve the robot construction problem.

Design and Manufacturing of a Tennis Ball Collecting Robot
Tao Chen, Matthew Stouder, Zhedong Han, Duankang Fu, Sara Lyons, Zhishang Xu
GEARE program, Purdue University, 2015
video / code

This project was the Senior Engineering Design Capstone project at Purdue University. We built an aesthetically pleasing tennis ball collecting robot which can collect tennis balls dispersed on a tennis court. I was fully responsible for all the programming and control tasks for the robot.

2015 RoboMaster Robotics Competition
Tao Chen, Mechanical Team Leader
hosted by DJI , 2015

This competition was a real-life version of Counter-Strike game with real mobile robots. I led and managed the mechanical group. We won the second prize in 2015 National RoboMaster Robotics Competition of east China and the third prize in 2015 National RoboMaster Robotics Competition Final.

An Active Rehabilitation Device for Elbow Joints
Tao Chen, Leader
Advisor: Prof. Hua Shao
Engineering Design, 2015
China Patent, CN105148460B
project page

We designed and built an inexpensive yet effective elbow joint rehabilitation device. The device is only composed of mechanical parts such as a lead screw, and a four-bar mechanism. It can help patients exercise their elbow joints in an inexpensive way, and it is also very easy to use and portable. We have applied a patent (Application Number: CN201510472161.5, Publication Number: CN105148460B) for this rehabilitation device.

A High-adaptability Rescue Robot
Tao Chen, Leader
Advisor: Prof. Qinghua Liang
Design and Manufacturing, 2014
code 1 / code 2

We built a high-adaptability track robot with two separate frames. I led and managed the team and I was responsible for electronic control, programming, and part of manufacturing.

Reviewer for ICLR (Outstanding Reviewer), NeurIPS, ICML, ICRA, RoboSoft, Humanoids, TPAMI, TII, etc.

This guy makes a nice webpage.