Robotics

*Outstanding Paper Award on Empirical Resourcefulness in RL* In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of simple …

Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. While previous work has focused on extensive modeling and simulation to find optimal controllers for …

Proximal policy optimization (PPO) has become one of the most popular deep reinforcement learning (DRL) algorithms. Yet, reproducing the PPO's results has been challenging in the community. While recent works conducted ablation studies to provide …

Stable-Baselines3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python. The implementations have been benchmarked against reference codebases, and automated unit tests cover 95% of the code. The algorithms …

We extend the original state-dependent exploration (SDE) to apply deep reinforcement learning algorithms directly on real robots. The resulting method, gSDE, yields competitive results in simulation but outperforms the unstructured exploration on the real robot.

Fault-Tolerant 6D Pose Estimation for Soft Robot. We present a simple ensembling method to detect and handle failures on a tendon driven robot.

We evaluate the benefits of decoupling feature extraction from policy learning in robotics and propose a new way of combining state representation learning methods.

Learning to drive smoothly in minutes using reinforcement learning on a Donkey Car.

Learning to drive smoothly in minutes using reinforcement learning on a Donkey Car.

S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics