Deep Learning for Robotics and Robotics for Deep Learning
Large-scale, data-driven techniques for robotic learning have imbued robots with unprecedented capabilities in recent years. However, a long-standing challenge for goal-directed learning on robots is the problem of supervision: how do we know if the robot actually achieved some goal, such as picking up the correct object? I present recent work in which we use robotic interaction to provide free supervision signals for deep representation learning, and reuse those very same representations to learn instance grasping. This synergy leads to interpretable visual representation learning and useful grasping skills, freeing us from ever having to label any data.
Eric is a research engineer on the Google Brain team, working on robotic grasping and manipulation. He is interested in meta-learning for robotics, deep generative models, and Artificial Life. He received his M.Sc. in CS and Bachelors in Math/CS at Brown University in 2016.