Ronald Fearing

Hierarchical Model-Based Reinforcement Learning with Temporal Abstractions

For long-horizon tasks in the real world, humans accumulate information over time and make inference using stored memory. An abstract transition model of the world at various time resolutions allows reasoning both globally and locally. Model-based reinforcement learning (MBRL) is efficient for learning policies for robotics control tasks. However, current MBRL approaches only predict future trajectories in a limited time span. A major reason is that the model only learns at one time resolution, typically at the environment transition frequency....

Safe and Sound: Learning Locomotion Skills Across Robot Morphology

Learning locomotion policies on real robots can be risky. Free robot exploration for policy learning in the real world is necessary but dangerous as it may cause catastrophes for the robots, especially for large, heavy and complicated robots. In this work, we aim at learning policies for robotic locomotion tasks for risky robots with the goal of minimizing the interaction of these risky robots with the environments. Though learning on large, heavy and complicated robots tends to be risky, learning policies on smaller, lighter and simpler robots has much lower cost and risk. We...