Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
arXiv:2009.05104
Abstract
Training agents to autonomously learn how to use anthropomorphic robotic hands has the potential to lead to systems capable of performing a multitude of complex manipulation tasks in unstructured and uncertain environments. In this work, we first introduce a suite of challenging simulated manipulation tasks that current reinforcement learning and trajectory optimisation techniques find difficult. These include environments where two simulated hands have to pass or throw objects between each other, as well as an environment where the agent must learn to spin a long pen between its fingers. We then introduce a simple trajectory optimisation that performs significantly better than existing methods on these environments. Finally, on the challenging PenSpin task we combine sub-optimal demonstrations generated through trajectory optimisation with off-policy reinforcement learning, obtaining performance that far exceeds either of these approaches individually, effectively solving the environment. Videos of all of our results are available at: https://dexterous-manipulation.github.io/
arXiv comment9 pages
References in corpus (14)
- Continuous control with deep reinforcement learning
- Addressing Function Approximation Error in Actor-Critic Methods
- Soft Actor-Critic Algorithms and Applications
- Solving Rubik's Cube with a Robot Hand
- Generative Adversarial Imitation Learning
- Learning Latent Dynamics for Planning from Pixels
- Hindsight Experience Replay
- Go-Explore: a New Approach for Hard-Exploration Problems
- Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
- Model Predictive Path Integral Control using Covariance Variable Importance Sampling
- Deep Dynamics Models for Learning Dexterous Manipulation
- Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration
- Goal-conditioned Imitation Learning
- Soft Hindsight Experience Replay