Perceiving and handling deformable objects is an integral part of everyday life for humans.
Automating tasks such as food handling, garment sorting, or assistive dressing requires …

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …

Deformable object manipulation (DOM) is an emerging research problem in robotics. The
ability to manipulate deformable objects endows robots with higher autonomy and promises …

Recent work has shown promising results for learning end-to-end robot policies using
imitation learning. In this work we address the question of how far can we push imitation …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

We present a survey of recent work on robot manipulation and sensing of deformable
objects, a field with relevant applications in diverse industries such as medicine (eg surgical …

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 …

Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or …

Recent approaches in robot perception follow the insight that perception is facilitated by
interaction with the environment. These approaches are subsumed under the term …