Learning to Explore in Motion and Interaction Tasks

Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems.

Authors

• Miroslav Bogdanovic
• Ludovic Righetti

Keywords

• Reinforcement learning
• Manipulators
• Data models
• Complexity theory
• Tuning
• Intelligent robots
• Convergence
• Motor Task
• Interaction Task
• Incremental Learning
• Robot Manipulator
• Exploration Strategy
• Contact Interaction
• Model-free Reinforcement Learning
• Problem In Robotics
• State Space
• Periodic Motion
• Reward Function
• Exploration Process
• Normal Force
• Deep Reinforcement Learning
• Reinforcement Learning Algorithm
• Previous Tasks
• Aspects Of The Task
• Real Robot
• Q-function
• Deterministic Policy
• Proximal Policy Optimization
• Ornstein-Uhlenbeck Process
• Goal Position
• Continuous Action Space
• Closed Curve
• Reward Information
• Policy Gradient Method
• Circular Path
• Contact Force
• Motion Primitives