Gabriel Margolis

I am a PhD student at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, advised by Pulkit Agrawal. I study learned control as a component of complete robotic systems. Previously, I received my BS ('20) and MEng ('21) degrees at MIT.

Updates

Research

Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation

Nolan Fey, Gabriel B. Margolis, Martin Peticco, Pulkit Agrawal

Preprint, 2025

Improve the sim-to-real transfer of a legged manipulator's athletic motor skills by aligning the physics of simulation to reality through a data-driven approach.

Position: Automatic Environment Shaping is the Next Frontier in RL

Younghyo Park*, Gabriel B. Margolis*, Pulkit Agrawal

International Conference on Machine Learning (ICML), 2024 (Oral Presentation)

To generalize the successes of reinforcement learning, we need research on the interaction between environment shaping and learning dynamics.

Action Space Design in Reinforcement Learning for Robot Motor Skills

Julian Eßer*, Gabriel B. Margolis*, Oliver Urbann, Sören Kerner, Pulkit Agrawal

Conference on Robot Learning (CoRL), 2024

Examine the mechanisms by which action space design impacts learning dynamics across robot embodiments, including evoBOT, a unique wheeled-legged biped.

Learning Force Control for Legged Manipulation

Tifanny Portela, Gabriel B. Margolis, Yandong Ji, Pulkit Agrawal

International Conference on Robotics and Automation (ICRA), 2024

Learn to control the force at the end effector of a quadruped robot with an arm for compliant and forceful loco-manipulation.

Maximizing Quadruped Velocity by Minimizing Energy

Srinath Mahankali*, Chi-Chang Lee*, Gabriel B. Margolis, Zhang-Wei Hong, Pulkit Agrawal

International Conference on Robotics and Automation (ICRA), 2024

Balance energy and velocity through constrained optimization for agile running with minimal reward shaping.

Learning to See Physical Properties with Active Sensing Motor Policies

Gabriel B. Margolis, Xiang Fu, Yandong Ji, Pulkit Agrawal

Conference on Robot Learning (CoRL), 2023

Learn to see how terrains feel by collecting self-supervised data with information-maximizing motor skills.

DribbleBot: Dynamic Legged Manipulation in the Wild

Yandong Ji*, Gabriel B. Margolis*, Pulkit Agrawal

International Conference on Robotics and Automation (ICRA), 2023

Dynamic object manipulation with the legs, using onboard computation and sensing.

Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior

Gabriel B. Margolis, Pulkit Agrawal

Conference on Robot Learning (CoRL), 2022 (Oral Presentation)

One learned policy embodies parameterized dynamic behaviors useful for different tasks.

Rapid Locomotion via Reinforcement Learning

Gabriel B. Margolis*, Ge Yang*, Kartik Paigwar, Tao Chen, Pulkit Agrawal

Robotics: Science and Systems (RSS), 2022

High-speed running and spinning on diverse terrains with a single neural network.

Learning to Jump from Pixels

Gabriel B. Margolis, Tao Chen, Kartik Paigwar, Xiang Fu, Donghyun Kim, Sangbae Kim, Pulkit Agrawal

Conference on Robot Learning (CoRL), 2021

A hierarchical control framework for dynamic vision-aware locomotion.