UC Berkeley

The robotics community has seen tremendous advances in grasping and manipulating a wide variety of objects using learning-based techniques. Deep learning approaches are often sample inefficient, and most work deals with grasping rigid objects rather than deformable objects such as ropes, clothing, and bags, which are ubiquitous in our daily lives. In this thesis, I will present novel approaches that can result in more efficient robot learning, including learning from simulation by using a skilled supervisor or building a dynamics model, learning using action-centric neural network architectures, and learning using a curriculum of samples. I will demonstrate results on manipulating deformable objects in simulation and real settings, and learning policies for Atari and MuJoCo environments.