Robotic manipulation challenges, such as grasping and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

While humans can use parts of their arms other than the hands for manipulations like
gathering and supporting, whether robots can effectively learn and perform the same type of …

Robots need robust models to effectively perform tasks that humans do on a daily basis.
These models often require substantial developmental costs to maintain because they need …

We study the problem of learning to perform multistage robotic manipulation tasks, with
applications to cable routing, where the robot must route a cable through a series of clips …

We address the problem of goal-directed cloth manipulation, a challenging task due to the
deformability of cloth. Our insight is that optical flow, a technique normally used for motion …

Deep learning models, though having achieved great success in many different fields over
the past years, are usually data-hungry, fail to perform well on unseen samples, and lack …

Deep imitation learning is promising for solving dexterous manipulation tasks because it
does not require an environment model and pre-programmed robot behavior. However, its …

Amidst the wide popularity of imitation learning algorithms in robotics, their properties
regarding hyperparameter sensitivity, ease of training, data efficiency, and performance …

Training language-conditioned policies is typically time-consuming and resource-intensive.
Additionally, the resulting controllers are tailored to the specific robot they were trained on …

Bimanual manipulation is essential in robotics, yet developing foundation models is
extremely challenging due to the inherent complexity of coordinating two robot arms …