With the rapid growth of computing powers and recent advances in deep learning, we have
witnessed impressive demonstrations of novel robot capabilities in research settings …

Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …

To enable general-purpose robots, we will require the robot to operate daily articulated
objects as humans do. Current robot manipulation has heavily relied on using a parallel …

Eye-in-hand cameras have shown promise in enabling greater sample efficiency and
generalization in vision-based robotic manipulation. However, for robotic imitation, it is still …

The field of visual representation learning has seen explosive growth in the past years, but
its benefits in robotics have been surprisingly limited so far. Prior work uses generic visual …

Learning from offline task demonstrations is a problem of great interest in robotics. For
simple short-horizon manipulation tasks with modest variation in task instances, offline …

Robotic manipulation can be formulated as inducing a sequence of spatial displacements:
where the space being moved can encompass an object, part of an object, or end effector. In …

Dexterous manipulation of arbitrary objects, a fundamental daily task for humans, has been
a grand challenge for autonomous robotic systems. Although data-driven approaches using …

The ability to leverage heterogeneous robotic experience from different robots and tasks to
quickly master novel skills and embodiments has the potential to transform robot learning …

Autonomous systems that efficiently utilize tools can assist humans in completing many
common tasks such as cooking and cleaning. However, current systems fall short of …