10 citations · 33 across the 33 of their papers we have counts for
25 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…
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…
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…
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…
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…
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…
Language-Conditioned Offline RL for Multi-Robot Navigation
Steven Morad, Ajay Shankar, Jan Blumenkamp +1
We present a method for developing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from…
The Cambridge RoboMaster: An Agile Multi-Robot Research Platform
Jan Blumenkamp, Ajay Shankar, Matteo Bettini +2
Compact robotic platforms with powerful compute and actuation capabilities are key enablers for practical, real-world deployments of multi-agent research. This article introduces a…
CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications
Jan Blumenkamp, Steven Morad, Jennifer Gielis +1
Autonomous robot operation in unstructured environments is often underpinned by spatial understanding through vision. Systems composed of multiple concurrently operating robots add…
Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigation
Zhan Gao, Guang Yang, Amanda Prorok
This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. The goal is to take both agent actions…
Modeling Aggregate Downwash Forces for Dense Multirotor Flight
Jennifer Gielis, Ajay Shankar, Ryan Kortvelesy +1
Dense formation flight with multirotor swarms is a powerful, nature-inspired flight regime with numerous applications in the realworld. However, when multirotors fly in close verti…
Docking Multirotors in Close Proximity using Learnt Downwash Models
Ajay Shankar, Heedo Woo, Amanda Prorok
Unmodeled aerodynamic disturbances pose a key challenge for multirotor flight when multiple vehicles are in close proximity to each other. However, certain missions \textit{require…
SO(2)-Equivariant Downwash Models for Close Proximity Flight
H. Smith, A. Shankar, J. Gielis +2
Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D forc…