Jason Ma

Hi there! I'm a final-year PhD student at UPenn GRASP Laboratory, where I am fortunate to be advised by Dinesh Jayaraman and Osbert Bastani. My research interests span robot learning, reinforcement learning, and foundation models for robotics. In particular, how to enable robots to learn from internet-scale multimodal data to unlock frontier and generalizable dexterous manipulation capabilities.

My research is supported by Apple Scholars in AI/ML PhD Fellowship. My work has received ICRA Best Paper Finalist as well as a best paper award at a CORL workshop. I am currently a student researcher at Google DeepMind; I have also interned at NVIDIA AI and Meta AI.

Google Scholar     Github     Twitter

yechengma at gmail dot com

Some recent research highlights:
Selected publications as first or last author; full list on Google Scholar.

(* indicates equal contribution, † indicates equal advising)

DrEureka: Language Model Guided Sim-To-Real Transfer

Jason Ma*, William Liang*, Hungju Wang, Sam Wang, Yuke Zhu, Linxi "Jim" Fan, Osbert Bastani, Dinesh Jayaraman
Webpage  •   Code

Eureka: Human-Level Reward Design via Coding Large Language Models

Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi "Jim" Fan†, Anima Anandkumar†
International Conference on Learning Representations (ICLR), 2024
★ Oral Presentation, NeurIPS Agent Learning in Open-Endedness Workshop ★
★ Oral Presentation, CORL Toward Generalist Robots Workshop ★
★ Oral Presentation, CORL Language and Robot Learning Workshop ★
Webpage  •   Arxiv  •   Code

Universal Visual Decomposer: Long-Horizon Manipulation Made Easy

Charles Zhang*, Yunshuang Li*, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Jason Ma†, Lucas Weihs†
International Conference on Robotics and Automation (ICRA), 2024
★ Best Paper Finalist in Robot Vision, ICRA 2024 ★
★ Best Paper Award, CORL LEAP Workshop ★
★ Oral Presentation, NeurIPS Foundation Models for Decision Making Workshop ★
Webpage  •   Arxiv  •   Code

LIV: Language-Image Representations and Rewards for Robotic Control

Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
International Conference on Machine Learning (ICML), 2023
★ Oral Presentation, RSS Workshop on Language for Robot Learning ★
Webpage  •   Arxiv  •   Code

VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar†, Amy Zhang†
International Conference on Learning Representations (ICLR), 2023
★ Spotlight Presentation, ICLR ★
★ Oral Presentation, NeurIPS Deep RL, Offline RL, FMDM Workshops★
★ Best Paper Finalist, NeurIPS Deep RL Workshop ★
Webpage  •   Arxiv  •   Code

How Far I'll Go: Offline Goal-Conditioned RL via F-Advantage Regression

Jason Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani
Neural Information Processing Systems (NeurIPS), 2022
★ Nominated for Outstanding Paper, NeurIPS ★
★ Best Paper Finalist, RSS Workshop on Learning from Diverse, Offline Data ★
Webpage  •   Arxiv  •   Code

TOM: Learning Policy-Aware Models for MBRL via Transition Occupancy Matching

Jason Ma*, Kausik Sivakumar*, Jason Yan, Osbert Bastani, Dinesh Jayaraman
Learning for Decision and Control (L4DC), 2023
Webpage  •   Arxiv  •   Code

SMODICE: Versatile Offline Imitation from Observations and Examples

Jason Ma, Andrew Shen, Dinesh Jayaraman, Osbert Bastani
International Conference on Machine Learning (ICML), 2022
Webpage  •   Arxiv  •   Code

CAP: Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Jason Ma*, Andrew Shen*, Osbert Bastani, Dinesh Jayaraman
Association for the Advancement of Artificial Intelligence (AAAI), 2022
Arxiv  •   Code

Likelihood-Based Diverse Sampling for Trajectory Forecasting

Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman, Osbert Bastani
International Conference on Computer Vision (ICCV), 2021
Arxiv  •   Code

Conservative Offline Distributional Reinforcement Learning

Jason Ma, Dinesh Jayaraman, Osbert Bastani
Neural Information Processing Systems (NeurIPS), 2021
Arxiv  •   Code


Amazon Robotics

Stanford Vision and Learning Lab

Johns Hopkins University

University of Michigan



UIUC Robot Learning Seminar

Northwestern Ability Lab

Johns Hopkins University Neuro AI

HKUST Info. Hub Seminar

Tsinghua University Yang Gao Lab

UT Austin MIDI Group

Intel AI Seminar


University of Edinburgh RL Seminar

MILA RL Seminar

UPenn GRASP SFI Seminar

Guest Lecture at UPenn CIS 519: Applied Machine Learning


Co-Organizer, NeurIPS Workshop on Goal-Conditioned Reinforcement Learning



I am looking to mentor highly motivated students to work on research projects all year long. I especially encourage students from underrepresented groups to get involved! If you are interested, please send me an email with your CV and your research interests.


William Liang

Johnny Wang

Fiona Luo

Lucy Lu