Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

Traditional robots have rigid underlying structures that limit their ability to interact with their
environment. For example, conventional robot manipulators have rigid links and can …

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 …

Cables are complex, high-dimensional, and dynamic objects. Standard approaches to
manipulate them often rely on conservative strategies that involve long series of very slow …

Using visual model-based learning for deformable object manipulation is challenging due to
difficulties in learning plannable visual representations along with complex dynamic models …

In this paper we tackle the problem of deformable object manipulation through model-free
visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we …

We demonstrate model-based, visual robot manipulation of deformable linear objects. Our
approach is based on a state-space representation of the physical system that the robot …

We present a novel vision-based grasp point detection algorithm that can reliably detect the
corners of a piece of cloth, using only geometric cues that are robust to variation in texture …

Robotic manipulation of deformable linear objects (DLOs) is important in many applications
such as the assembly of deformable wire harnesses and cables in manufacturing. Despite …

Unlike traditional rigid linked robots, soft robotic manipulators can bend into a wide variety of
complex shapes due to control inputs and gravitational loading. This paper presents a new …