Bootstrapping Motor Skill Learning with Motion Planning

Learning a robot motor skill from scratch is impractically slow; so much so that in practice, learning must typically be bootstrapped using human demonstration. However, relying on human demonstration necessarily degrades the autonomy of robots that must learn a wide variety of skills over their operational lifetimes. We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation. We demonstrate the use of motion planners to bootstrap motor skills in two complex object manipulation scenarios with different policy representations: opening a drawer with a dynamic movement primitive representation, and closing a microwave door with a deep neural network policy. We also show how our method can bootstrap a motor skill for the challenging dynamic task of learning to hit a ball off a tee, where a kinematic plan based on treating the scene as static is insufficient to solve the task, but sufficient to bootstrap a more dynamic policy. In all three cases, our method is competitive with human-demonstrated initialization, and significantly out-performs starting with a random policy. This approach enables robots to to efficiently and autonomously learn motor policies for dynamic tasks without human demonstration.

Authors

• Ben Abbatematteo
• Eric Rosen
• Stefanie Tellex
• George Konidaris 0001

Keywords

• Dynamics
• Kinematics
• Reinforcement learning
• Microwave theory and techniques
• Search problems
• Planning
• Noise measurement
• Motor Skills
• Path Planning
• Neural Network
• Deep Neural Network
• Robot Motion
• Autonomous Learning
• Dynamic Policy
• Simulation Experiments
• Model Predictive Control
• Reward Function
• Configuration Space
• Objective Conditions
• Random Initialization
• Policy Learning
• Policy Gradient
• Real-world Tasks
• Simulated Task
• Object Pose
• Joint Configuration
• Policy Search
• Inverse Reinforcement Learning
• Use Of Motion
• Robot Kinematics
• Model-free Reinforcement Learning
• Real Hardware
• Robot Learning
• Real-world Experiments
• Complex Search
• Results Of Experiments