6 papers
LIMBO: Learning and Internalizing Model-Free Barrier Objectives for Agile and Safe Whole-Body Control
Jake Gonzales, Arturo Flores Alvarez, Yu-Ming Chen +3
Safe whole-body control requires coordinating collision avoidance and balance under high-dimensional, nonlinear dynamics--making safety certificates difficult to design and reuse a…
Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Max Horwitz, Jake Gonzales, Eric Mazumdar +1
A growing line of work reframes preference-based fine-tuning of large language models game-theoretically: Nash Learning from Human Feedback (NLHF) recasts the problem as a zero-sum…
Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Control
Jake Gonzales, Kazuki Mizuta, Karen Leung +1
In this paper, we present a novel probabilistic safe control framework for human-robot interaction that combines control barrier functions (CBFs) with conformal risk control to pro…
Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
Jake Gonzales, Max Horwitz, Eric Mazumdar +1
Provably efficient and robust equilibrium computation in general-sum Markov games remains a core challenge in multi-agent reinforcement learning. Nash equilibrium is computationall…
How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systems
Oswin So, Zachary Serlin, Makai Mann +4
Control barrier functions (CBF) have become popular as a safety filter to guarantee the safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to cons…
Data-Driven Stochastic Optimal Control Using Kernel Gradients
Adam J. Thorpe, Jake A. Gonzales, Meeko M. K. Oishi
We present an empirical, gradient-based method for solving data-driven stochastic optimal control problems using the theory of kernel embeddings of distributions. By embedding the…