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Found 8 papers, 3 papers with code
See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation
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.
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
VMAS's scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms.
A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
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.
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.
Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication
In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems.
The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning
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
End to end collision avoidance based on optical flow and neural networks
Optical flow is believed to play an important role in the agile flight of birds and insects.
Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation
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.
