6 papers
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…
A Player Selection Network for Scalable Game-Theoretic Prediction and Planning
Tianyu Qiu, Eric Ouano, Fernando Palafox +2
While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increase…
Generalized Information Gathering Under Dynamics Uncertainty
Fernando Palafox, Jingqi Li, Jesse Milzman +1
An agent operating in an unknown dynamical system must learn its dynamics from observations. Active information gathering accelerates this learning, but existing methods derive bes…
Data-Driven Modeling and Correction of Vehicle Dynamics
Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj +4
We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exh…
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
Jingqi Li, Chih-Yuan Chiu, Lasse Peters +6
Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochast…
Smooth Information Gathering in Two-Player Noncooperative Games
Fernando Palafox, Jesse Milzman, Dong Ho Lee +2
We present a mathematical framework for modeling two-player noncooperative games in which one player is uncertain of the other player's costs but can preemptively allocate informat…