Humans throw and catch objects all the time. However, such a seemingly common skill
introduces a lot of challenges for robots to achieve: The robots need to operate such …

We propose a learning-based system for enabling quadrupedal robots to manipulate large,
heavy objects using their whole body. Our system is based on a hierarchical control strategy …

Deformable linear object (DLO) manipulation is needed in many fields. Previous research
on deformable linear object (DLO) manipulation has primarily involved parallel jaw gripper …

We present an approach to learn general robot manipulation priors from 3D hand-object
interaction trajectories. We build a framework to use in-the-wild videos to generate …

This paper identifies and addresses the problems with naively combining (reinforcement)
learning-based controllers and state estimators for robotic in-hand manipulation …

Sample efficiency remains a crucial challenge in applying Reinforcement Learning (RL) to
real-world tasks. While recent algorithms have made significant strides in improving sample …

Achieving human-like dexterous manipulation remains a crucial area of research in robotics.
Current research focuses on improving the success rate of pick-and-place tasks. Compared …

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of
learning complex manipulation behaviors on an arm equipped with a three-fingered robotic …

Despite extreme sample inefficiency, on-policy reinforcement learning, aka policy gradients,
has become a fundamental tool in decision-making problems. With the recent advances in …

In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving
complex continuous control tasks like locomotion and dexterous manipulation. However, this …