7 citations · 21 across the 20 of their papers we have counts for
11 papers · 1 filter
Residual Denoising Enables Sample-Efficient Multi-Agent Coordination on Demand
Dayi Dong, Maulik Bhatt, Aayushi Shrivastava +2
Pretrained robot policies offer strong manipulation skills but are typically limited to single-agent settings, where a robot acts in isolation. In this work, we study how to adapt…
Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations
Lasse Peters, Laura Ferranti, Andrea Bajcsy +1
Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems, like multiple arms or vehic…
Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing
Andrei-Carlo Papuc, Lasse Peters, Sihao Sun +2
Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requires drones not only to navigate a…
Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis
Kensuke Nakamura, Lasse Peters, Andrea Bajcsy
Hamilton-Jacobi (HJ) reachability is a rigorous mathematical framework that enables robots to simultaneously detect unsafe states and generate actions that prevent future failures.…
Updating Robot Safety Representations Online from Natural Language Feedback
Leonardo Santos, Zirui Li, Lasse Peters +2
Robots must operate safely when deployed in novel and human-centered environments, like homes. Current safe control approaches typically assume that the safety constraints are know…
Auto-Encoding Bayesian Inverse Games
Xinjie Liu, Lasse Peters, Javier Alonso-Mora +2
When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling. In in…
Contingency Games for Multi-Agent Interaction
Lasse Peters, Andrea Bajcsy, Chih-Yuan Chiu +4
Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under un…
Online and Offline Learning of Player Objectives from Partial Observations in Dynamic Games
Lasse Peters, Vicenç Rubies-Royo, Claire J. Tomlin +4
Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scena…
Learning to Play Trajectory Games Against Opponents with Unknown Objectives
Xinjie Liu, Lasse Peters, Javier Alonso-Mora
Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planni…
Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations
Lasse Peters, David Fridovich-Keil, Vicenç Rubies-Royo +2
Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theor…
Inference-Based Strategy Alignment for General-Sum Differential Games
Lasse Peters, David Fridovich-Keil, Claire J. Tomlin +1
In many settings where multiple agents interact, the optimal choices for each agent depend heavily on the choices of the others. These coupled interactions are well-described by a…