Search Results for author:
Found 4 papers, 1 papers with code
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios
We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems.
Learning Resilient Behaviors for Navigation Under Uncertainty
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically.
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system.
Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations.
