no code implementations • 1 Aug 2022 • Jan Blumenkamp, QingBiao Li, Binyu Wang, Zhe Liu, Amanda Prorok
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 access to global positioning information and only use first-person-view images.
1 code implementation • 7 Jul 2022 • Matteo Bettini, Ryan Kortvelesy, Jan Blumenkamp, Amanda Prorok
VMAS's scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms.
2 code implementations • 2 Nov 2021 • Jan Blumenkamp, Steven Morad, Jennifer Gielis, QingBiao Li, Amanda Prorok
We demonstrate our framework on a case-study that requires tight coordination between robots, and present first-of-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication.
no code implementations • 26 Jul 2021 • Amanda Prorok, Jan Blumenkamp, QingBiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.
no code implementations • 1 Dec 2020 • Rupert Mitchell, Jan Blumenkamp, Amanda Prorok
In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems.
1 code implementation • 6 Aug 2020 • Jan Blumenkamp, Amanda Prorok
Such a design choice, however, precludes the existence of a single, differentiable communication channel, and consequently prohibits the learning of inter-agent communication strategies.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 6 Nov 2019 • Jan Blumenkamp
Optical flow is believed to play an important role in the agile flight of birds and insects.
no code implementations • 4 Nov 2019 • Jan Blumenkamp, Andreas Baude, Tim Laue
Deep learning approaches have become the standard solution to many problems in computer vision and robotics, but obtaining sufficient training data in high enough quality is challenging, as human labor is error prone, time consuming, and expensive.