10 citations · 33 across the 33 of their papers we have counts for
13 papers · 1 filter
Learning Robot Behavior Priors from Non-Robotic Synthetic Data
Mateusz Odrowaz-Sypniewski, Michael Amir, Jasmine Bayrooti +2
Real robot data, rich across tasks and morphologies, is often treated as the bottleneck for training robot learning models, including embodied foundation models that aim for zero-s…
Weakly Supervised Discovery of Motion Styles for Compositional Robot Control
Heedo Woo, Antonio Marino, Eduardo Sebastián +1
When a robot possesses more degrees of freedom than a task requires, multiple trajectories can achieve the same objective using distinct motion styles. Compositional robot control…
GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories
Rishabh Jain, Akmaral Moldagalieva, Lorenzo Magnino +5
GAMBIT is an opening chess move in which a player sacrifices a piece, typically a pawn, to gain a positional advantage later in the game. Analogously, in multi-robot coordination,…
Denoising Multi-Robot Trajectories
Yuhao Zhang, Keisuke Okumura, Ajay Shankar +1
Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multimodal, and high-dimensional…
Remotely Detectable Keyed Communication through Motion
Benjamin Chang, Michael Amir, Manon Flageat +1
Messages from electronic devices are conventionally received as text, audio, or radio signals. But robots move with rich, articulate motion in the real world, opening up the possib…
World-Task Factorization for Robot Learning
Eduardo Sebastián, Adrian Pfisterer, Vito Mengers +2
Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments. To achieve this, we must structurally factor the policy, which…
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar +1
Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent model…
Events as Triggers for Behavioral Diversity in Multi-Agent Reinforcement Learning
Hannes Büchi, Manon Flageat, Eduardo Sebastián +1
Effective multi-agent cooperation requires agents to adopt diverse behaviors as task conditions evolve-and to do so at the right moment. Yet, current Multi-Agent Reinforcement Lear…
Differentiable Environment-Trajectory Co-Optimization for Safe Multi-Agent Navigation
Zhan Gao, Gabriele Fadini, Stelian Coros +1
The environment plays a critical role in multi-agent navigation by imposing spatial constraints, rules, and limitations that agents must navigate around. Traditional approaches tre…
Wake Up to the Past: Using Memory to Model Fluid Wake Effects on Robots
Luca Vendruscolo, Eduardo Sebastián, Amanda Prorok +1
Autonomous aerial and aquatic robots that attain mobility by perturbing their medium, such as multicopters and torpedoes, produce wake effects that act as disturbances for adjacent…
Decomposing Private Image Generation via Coarse-to-Fine Wavelet Modeling
Jasmine Bayrooti, Weiwei Kong, Natalia Ponomareva +3
Generative models trained on sensitive image datasets risk memorizing and reproducing individual training examples, making strong privacy guarantees essential. While differential p…
From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking
Chuwei Wang, Eduardo Sebastián, Amanda Prorok +1
Robotic navigation has historically struggled to reconcile reactive, sensor-based control with the decisive capabilities of model-based planners. This duality becomes critical when…
Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding
Rishabh Jain, Keisuke Okumura, Michael Amir +2
Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. S…