Learning Terrain Representation for Legged Robot Localization In this talk, I will describe a haptic localization system based on a terrain representation learned from the force/torque sensors located at the quadruped robot’s feet. Our approach was twofold, supervised terrain classification […]
Greedy Robots in a Changing World – Forgetting and Transfer in Continual Reinforcement Learning Although the world we’re living in is changing rapidly, deep learning models are designed primarily for static data and stationary environments. In my talk, I will […]
Be like water: Adaptive Subgoal Search Reinforcement learning involves training agents to interact with an environment through a feedback loop. To optimize performance, the agent must adapt to various information streams, such as environment changes, experience, uncertainty, and task complexity. […]
