Deformable One-Dimensional Object Detection for Routing and Manipulation

Recursive Learning for Deformable Object Manipulation

This paper presents a generalized approach to handling of 3D deformable objects. Our task is to learn robotic grasping characteristics for a non-rigid object represented by a physically-based model. The model is derived from discretizing the object into a network of interconnected particles and springs. Using Newtonian equations, we model the particle motion of a deformable object and thus calc...

Intelligent learning for deformable object manipulation

The majority of manipulation systems are designed with the assumption that the objects’being handled are rigid and do not deform when grasped. This paper addresses the problem of robotic grasping and manipulation of 3-D deformable objects, such as rubber balls or bags filled with sand.‘ Specifically, we have developed a generalized learning algorithm for handling of 3-D deformable objects in wh...

Interleaving Planning and Control for Deformable Object Manipulation

Bandit-Based Model Selection for Deformable Object Manipulation

We present a novel approach to deformable object manipulation that does not rely on highly-accurate modeling. The key contribution of this paper is to formulate the task as a Multi-Armed Bandit problem, with each arm representing a model of the deformable object. To “pull” an arm and evaluate its utility, we use the arm’s model to generate a velocity command for the gripper(s) holding the objec...

Learning to Select Expert Demonstrations for Deformable Object Manipulation

Automated manipulation of deformable objects tends to be challenging due to high-dimensional, continuous state-action spaces and due to the complicated dynamics of deformable objects. Direct planning or optimal control techniques are often intractable for this setting. Despite these challenges, recent work [2, 3] has leveraged expert demonstrations to make progress on robotic manipulation of de...