Tao Chen (陈涛)

I received my Ph.D. degree in EECS at MIT CSAIL, advised by Prof. Pulkit Agrawal. My research interests revolve around robot learning (dexterous manipulation, locomotion, 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 /  Twitter

News


Preprint
Vegetable Peeling: A Case Study in Constrained Dexterous Manipulation
Tao Chen, Eric Cousineau, Naveen Kuppuswamy, Pulkit Agrawal
project page / arXiv

A robotic system that peels vegetables with a dexterous robot hand.

Research
Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes
Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Edward Adelson, Pulkit Agrawal
Science Robotics, 2023
Science Robotics paper / arXiv / project page / code / bibtex @article{chen2023visual,
author = {Tao Chen and Megha Tippur and Siyang Wu and Vikash Kumar and Edward Adelson and Pulkit Agrawal },
title = {Visual dexterity: In-hand reorientation of novel and complex object shapes},
journal = {Science Robotics},
volume = {8},
number = {84},
pages = {eadc9244},
year = {2023},
doi = {10.1126/scirobotics.adc9244},
URL = {https://www.science.org/doi/abs/10.1126/scirobotics.adc9244},
eprint = {https://www.science.org/doi/pdf/10.1126/scirobotics.adc9244},
}

A real-time controller that dynamically reorients complex and novel objects by any amount using a single depth camera.

Reconciling Reality Through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
Marcel Torne, Anthony Simeonov, Zechu Li, April Chan, Tao Chen, Abhishek Gupta, Pulkit Agrawal
Robotics: Science and Systems (RSS) , 2024
arXiv / project page

Improve the robustness of imitation learning policies with a real-to-sim-to-real approach.

sym Lifelong Robot Learning with Human Assisted Language Planners
Meenal Parakh*, Alisha Fong*, Anthony Simeonov, Tao Chen, Abhishek Gupta, Pulkit Agrawal
(*equal contribution)
International Conference on Robotics and Automation (ICRA), 2023

paper / project page / bibtex @article{parakh2023human,
title={Lifelong Robot Learning with Human Assisted Language Planners},
author={Parakh, Meenal and Fong, Alisha and Simeonov, Anthony and Chen, Tao and Gupta, Abhishek and Agrawal, Pulkit},
journal={arXiv preprint arXiv:2309.14321},
year={2023}
} }

An LLM-based task planner that can learn new skills opens doors for continual learning.

Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
Marcel Torne, Max Balsells, Zihan Wang, Samedh Desai, Tao Chen, Abhishek Gupta
Advances in Neural Information Processing Systems (NeurIPS), 2023
paper / project page / code / bibtex @article{torne2023breadcrumbs,
title={Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback},
author={Torne, Marcel and Balsells, Max and Wang, Zihan and Desai, Samedh and Chen, Tao and Agrawal, Pulkit and Gupta, Abhishek},
journal={Advances in Neural Information Processing Systems},
year={2023}
} }


Press coverage: MIT News

Method for guiding goal-directed exploration with asynchronous human feedback.

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)
paper / arXiv / bibtex / code / 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},
year={2021}
}


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 Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
Zechu Li*, Tao Chen*, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal
(* indicates equal contribution)
International Conference on Machine Learning (ICML) , 2023
bibtex / arXiv / Code @article{li2023parallel,
title={Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation},
author={Li, Zechu and Chen, Tao and Hong, Zhang-Wei and Ajay, Anurag and Agrawal, Pulkit},
journal={International Conference on Machine Learning},
year={2023}
}

Scale up off-policy algorithms with more than 10K parallel environments on a single workstation

TactoFind: A Tactile Only System for Object Retrieval
Sameer Pai*, Tao Chen*, Megha Tippur*, Edward Adelson, Abhishek Gupta, Pulkit Agrawal
(* indicates equal contribution, † indicates equal advising)
International Conference on Robotics and Automation (ICRA) , 2023
bibtex / project page / arXiv @article{pai2022tactofind,
title={TactoFind: A Tactile Only System for Object Retrieval},
author={Pai, Sameer and Chen, Tao and Tippur, Megha and Adelson, Edward and Gupta, Abhishek and Agrawal, Pulkit},
journal={International Conference on Robotics and Automation},
year={2023}
}

Localize, identify, and fetch a target object in the dark with tactile sensors

sym DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Sizhe Li, Zhiao Huang, Tao Chen, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan

International Conference on Learning Representations (ICLR) , 2023
bibtex / arXiv / Code / Project page @article{li2023dexdeform,
title={DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics},
author={Li, Sizhe and Huang, Zhiao and Chen, Tao and Du, Tao and Su, Hao and Tenenbaum, Joshua B and Gan, Chuang},
journal={International Conference on Learning Representations},
year={2023}
}

Dexterous manipulation with deformable objects

sym ConceptFusion: Open-set Multimodal 3D Mapping
Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd Omama, Tao Chen, Shuang Li, Ganesh Iyer, Soroush Saryazdi, Nikhil Keetha, Ayush Tewari, Joshua B. Tenenbaum, Celso Miguel de Melo, Madhava Krishna, Liam Paull, Florian Shkurti, Antonio Torralba

Robotics: Science and Systems (RSS) , 2023
bibtex / arXiv / Code / Project page @article{jatavallabhula2023conceptfusion,
title={Conceptfusion: Open-set multimodal 3d mapping},
author={Jatavallabhula, {Krishna Murthy} and Kuwajerwala, Alihusein and Gu, Qiao and Omama, Mohd and Chen, Tao and Li, Shuang and Iyer, Ganesh and Saryazdi, Soroush and Keetha, Nikhil and Tewari, Ayush and Tenenbaum, {Joshua B.} and {de Melo}, {Celso Miguel} and Krishna, Madhava and Paull, Liam and Shkurti, Florian and Torralba, Antonio},
journal={Robotics: Science and Systems},
year={2023}
}

A scene representation that allows for multi-modal (language, image, audio, etc.) and open-set queries

sym Efficient Tactile Simulation with Differentiability for Robotic Manipulation
Jie Xu, Sangwoon Kim, Tao Chen, Alberto Rodriguez, Pulkit Agrawal, Wojciech Matusik, Shinjiro Sueda
Conference on Robot Learning (CoRL), 2022
paper / bibtex / project page / @inproceedings{xu2022efficient,
title={Efficient Tactile Simulation with Differentiability for Robotic Manipulation},
author={Jie Xu and Sangwoon Kim and Tao Chen and Alberto Rodriguez Garcia and Pulkit Agrawal and Wojciech Matusik and Shinjiro Sueda},
booktitle={6th Annual Conference on Robot Learning},
year={2022},
url={https://openreview.net/forum?id=6BIffCl6gsM}
}

Tactile Simulator for complex shapes training on which transfers to real-world.

sym Rapid Locomotion via Reinforcement Learning
Gabriel Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal
Robotics: Science and Systems (RSS), 2022
paper / project page / bibtex / video / @article{margolis2022rapid,
title={Rapid Locomotion via Reinforcement Learning},
author={Margolis, Gabriel B and Yang, Ge and Paigwar, Kartik and Chen, Tao and Agrawal, Pulkit},
journal={Robotics: Science and Systems},
year={2022}
}


Press coverage: Wired, Popular Science, TechCrunch, BBC , MIT News

High-speed running and spinning on diverse terrains with a RL based controller.

HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
Yu-Wei Chao, Chris Paxton, Yu Xiang, Wei Yang, Balakumar Sundaralingam, Tao Chen, Adithya Murali, Maya Cakmak, Dieter Fox
International Conference on Robotics and Automation (ICRA) , 2022
arXiv / bibtex / project page / code @article{chao2022handoversim,
title={HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers},
author={Chao, Yu-Wei and Paxton, Chris and Xiang, Yu and Yang, Wei and Sundaralingam, Balakumar and Chen, Tao and Murali, Adithyavairavan and Cakmak, Maya and Fox, Dieter},
journal={International Conference on Robotics and Automation},
year={2022}
}


1000 handover scenes captured from the real world, reproduced in simulation

sym Topological Experience Replay
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal
International Conference on Learning Representations (ICLR), 2022
paper / bibtex / video / @inproceedings{
hong2022topological,
title={Topological Experience Replay},
author={Hong, Zhang-Wei and Chen, Tao and Lin, Yen-Chen and Pajarinen, Joni and Agrawal, Pulkit},
booktitle={In Proceedings of The Tenth International Conference on Learning Representations },
year={2022},
url={https://openreview.net/forum?id=OXRZeMmOI7a},
}

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

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},
year={2021}
}


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

A hierarchical control framework for dynamic vision-aware locomotion.

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},
year={2021}
}


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},
year={2021}
}

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},
pages={9355--9366},
year={2018}
}

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.

Teaching

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

MIT 6.S090 Deep Learning for Control - IAP 2021
Instructor

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
video

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.

Service
Reviewer for ICLR (Outstanding Reviewer), CoRL, NeurIPS, ICML, RSS, ICRA, IROS, RoboSoft, Humanoids, TPAMI, TII, T-RO etc.

This guy makes a nice webpage.