Robot Deep Reinforcement Learning: Tensor State-Action Spaces and Auxiliary Task Learning with Multiple State Representations
Zoom Link Abstract: A long standing goal of robotics research is to create algorithms that can automatically learn complex control strategies from scratch. Part of the challenge of applying such algorithms to robots is the choice of representation. Reinforcement Learning (RL) algorithms have been successfully applied to many different robotic tasks such as the Ball-in-a-Cup [...]
Raunaq Bhirangi – MSR Thesis Talk
Zoom link: https://cmu.zoom.us/j/93803046130?pwd=dE5LU21lakcxNjBmZ0EvVDdNOWswdz09 Title: Learning Families of Behaviors for Legged Locomotion using Model-Free Deep Reinforcement Learning Abstract: Conventional planning and control of highly articulated legged robots is challenging because of the high dimensionality of the state space, and such conventional techniques normally produce a single point solution. In this work, we present a [...]
William Qi – MSR Thesis Talk
Location: https://cmu.zoom.us/j/96923127678?pwd=TWt3Zk5neFUzSlJWUjZEN2F6UVhudz09 Title: Representation Learning for Safe Autonomous Movement Abstract: Mobile robots have become an increasingly common presence in our homes and on our roads. To move safely within these shared spaces, autonomous agents must understand how other dynamic actors behave and how such behavior influences the navigability of the surrounding scene. Towards this goal, we [...]