10 citations · 33 across the 33 of their papers we have counts for
13 papers · 1 filter
Remotely Detectable Robot Policy Watermarking
Michael Amir, Manon Flageat, Amanda Prorok
The success of machine learning for real-world robotic systems has created a new form of intellectual property: the trained policy. This raises a critical need for novel methods th…
db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF
Akmaral Moldagalieva, Keisuke Okumura, Amanda Prorok +1
State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in s…
Scaling Multi-Agent Environment Co-Design with Diffusion Models
Hao Xiang Li, Michael Amir, Amanda Prorok
The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performance. With application domains ranging…
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
Jasmine Bayrooti, Sattar Vakili, Amanda Prorok +1
Thompson sampling (TS) is a powerful and widely used strategy for sequential decision-making, with applications ranging from Bayesian optimization to reinforcement learning (RL). D…
Graph Attention-Guided Search for Dense Multi-Agent Pathfinding
Rishabh Jain, Keisuke Okumura, Michael Amir +1
Finding near-optimal solutions for dense multi-agent pathfinding (MAPF) problems in real-time remains challenging even for state-of-the-art planners. To this end, we develop a hybr…
Prompting Robot Teams with Natural Language
Eduardo Sebastián, Nicolas Pfitzer, Ajay Shankar +1
This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities of language…
ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
Michael Amir, Guang Yang, Zhan Gao +3
Constraint-based optimization is a cornerstone of robotics, enabling the design of controllers that reliably encode task and safety requirements such as collision avoidance or form…
LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
Ajay Shankar, Keisuke Okumura, Amanda Prorok
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework inv…
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
Michael Amir, Matteo Bettini, Amanda Prorok
The success of teams in robotics, nature, and society often depends on the division of labor among diverse specialists; however, a principled explanation for when such diversity su…
D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations
Yuhao Zhang, Keisuke Okumura, Heedo Woo +2
This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffu…
DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems
Joshua Bird, Jan Blumenkamp, Amanda Prorok
Cooperative Simultaneous Localization and Mapping (C-SLAM) enables multiple agents to work together in mapping unknown environments while simultaneously estimating their own positi…
Distributed Coverage Control for Time-Varying Spatial Processes
Federico Pratissoli, Mattia Mantovani, Amanda Prorok +1
Multi-robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study…
The impact of behavioral diversity in multi-agent reinforcement learning
Matteo Bettini, Ryan Kortvelesy, Amanda Prorok
Many of the world's most pressing issues, such as climate change and global peace, require complex collective problem-solving skills. Recent studies indicate that diversity in indi…