Search Results for author:
Found 8 papers, 3 papers with code
DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator
We propose DiffSRL, a dynamic state representation learning pipeline utilizing differentiable simulation that can embed complex dynamics models as part of the end-to-end training.
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.
Modeling 3D Shapes by Reinforcement Learning
We explore how to enable machines to model 3D shapes like human modelers using deep reinforcement learning (RL).
Learning Resilient Behaviors for Navigation Under Uncertainty
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically.
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL).
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
We built neural networks as our policy to map sensor readings to control signals on the UAV.
Safe Navigation with Human Instructions in Complex Scenes
In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons by following human instructions such as "go to the restaurant and keep away from people".
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.
