Learning distributed control for modular robots

We propose to automate controller design for distributed modular robots. In this paper, we present some initial experiments with learning distributed controllers for synthesizing compliant locomotion gaits for modular, self-reconfigurable robots. We use both centralized and distributed policy search and find that the learning approach is promising, as locomotion tasks are learnt well. We also find that the additive nature of the robotic platforms can help speed up learning if we increase the robot size incrementally.

Authors

• Paulina Varshavskaya
• Leslie Pack Kaelbling
• Daniela Rus

Keywords

• Distributed control
• Robotics and automation
• Automatic control
• Robot control
• Shape control
• Learning
• Distributed algorithms
• Robot sensing systems
• Size control
• Control systems
• Modular Robots
• Locomotion Tasks
• Policy Search
• Learning Algorithms
• Center Of Mass
• Local Optimum
• Lookup Table
• Markov Decision Process
• Convergence Time
• Transition Function
• Distributed Algorithm
• Policy Space
• Reward Signal
• End Of Episode
• Gradient Ascent
• Physical Robot
• Physical Coupling
• Agent Observes
• Increasing Task Complexity