Publications (7)
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
Jacob Levy, Jason Gibson, Bogdan Vlahov +4
High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics and the difficulty of accurately modeling terrain-vehicle interac…
Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a pro…
Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models
Jacob Levy, Tyler Westenbroek, David Fridovich-Keil
Traditionally, model-based reinforcement learning (MBRL) methods exploit neural networks as flexible function approximators to represent unknown environment dyn…
Active Inverse Learning in Stackelberg Trajectory Games
William Ward, Yue Yu, Jacob Levy +3
Game-theoretic inverse learning is the problem of inferring a player's objectives from their actions. We formulate an inverse learning problem in a Stackelberg game between a leade…
Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
Arthur Bouton, Tristan D. Hasseler, Michael Paton +5
This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable whe…
Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
Jacob Levy, Tyler Westenbroek, Kevin Huang +6
Robot learning requires adaptation methods that improve reliably from limited, mixed-quality interaction data. This is especially challenging in long-horizon, contact-rich tasks, w…