21 citations · 49 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation
Jan Blumenkamp, Qingbiao Li, Binyu Wang +2
We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have ac…
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp +1
While many multi-robot coordination problems can be solved optimally by exact algorithms, solutions are often not scalable in the number of robots. Multi-Agent Reinforcement Learni…
A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
Jan Blumenkamp, Steven Morad, Jennifer Gielis +2
GNNs are a paradigm-shifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as f…
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
Amanda Prorok, Jan Blumenkamp, Qingbiao Li +3
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of le…
Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication
Rupert Mitchell, Jan Blumenkamp, Amanda Prorok
In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems. Specifically, we propose a solution towards robust cooperation,…
The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning
Jan Blumenkamp, Amanda Prorok
Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication s…
End to end collision avoidance based on optical flow and neural networks
Jan Blumenkamp
Optical flow is believed to play an important role in the agile flight of birds and insects. Even though it is a very simple concept, it is rarely used in computer vision for colli…