Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts

By Nikhil Chavan Dafle, Alberto Rodriguez. 2017

38 citations recorded by the source corpus.

This paper presents an approach to in-hand manipulation planning that exploits the mechanics of alternating sticking contact. Particularly, we consider the problem of manipulating a grasped object using external pushes for which the pusher sticks to the object. Given the physical properties of the object, frictional coefficients at contacts and a desired regrasp on the object, we propose a sampling-based planning framework that builds a pushing strategy concatenating different feasible stable pushes to achieve the desired regrasp. An efficient dynamics formulation allows us to plan in-hand manipulations 100–1000 times faster than our previous work which builds upon a complementarity formulation. Experimental observations for the generated plans show that the object precisely moves in the grasp as expected by the planner. Video Summary – youtu.be/qOTKRJMx6Ho

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Alberto Rodriguez
2. Nikhil Chavan Dafle
3. Kuan-Ting Yu

In Control and Systems Engineering

1. Alberto Rodriguez
2. Nikhil Chavan Dafle
3. Kuan-Ting Yu
4. Andy Zeng
5. Maria Bauzá

Citations in the corpus

• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (2018, neutral): “This stands in contrast to the kinds of grasping behaviors observed in humans and animals, where the grasp is a dynamical process that tightly interleaves sensing and control at every stage [9, 10].”
• Sampling-based Planning of In-Hand Manipulation with External Pushes (2017, background): “AB for flexibility and currently not optimized for time. One promising direction for faster planning is to limit the planner to a subset of pushing motions whose dynamics are less expensive to compute [3]. Another practical way is to extend this work to a multi-query framework to exploit the already ”