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In-hand manipulation using gravity and controlled slip

TLDR
This work proposes a sliding mode controller for in-hand manipulation that repositions a tool in the robot's hand by using gravity and controlling the slippage of the tool.
Abstract
In this work we propose a sliding mode controller for in-hand manipulation that repositions a tool in the robot's hand by using gravity and controlling the slippage of the tool. In our approach, the robot holds the tool with a pinch grasp and we model the system as a link attached to the gripper via a passive revolute joint with friction, i.e., the grasp only affords rotational motions of the tool around a given axis of rotation. The robot controls the slippage by varying the opening between the fingers in order to allow the tool to move to the desired angular position following a reference trajectory. We show experimentally how the proposed controller achieves convergence to the desired tool orientation under variations of the tool's inertial parameters.

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Citations
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Journal ArticleDOI

Dynamic In-Hand Sliding Manipulation

TL;DR: A limited surface model of the contact pressure distribution at each finger to predict the sliding directions is proposed and is applied to the problem of regrasping a laminar object held in a pinch grasp.
Posted Content

Reinforcement Learning for Pivoting Task

TL;DR: This work proposes an approach to learn a robust policy for solving the pivoting task and shows that several model-free continuous control algorithms were shown to learn successful policies with this approach.
Proceedings ArticleDOI

Adaptive control for pivoting with visual and tactile feedback

TL;DR: This work presents an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot's hand by means of gravity.
Journal ArticleDOI

Variable-Friction Finger Surfaces to Enable Within-Hand Manipulation via Gripping and Sliding

TL;DR: This letter presents a simple mechanical analogy to the human finger pad, via a robotic finger with both high- and low-friction surfaces, and demonstrates how within-hand rolling and sliding of an object may be achieved without the need for tactile sensing, high-dexterity, dynamic finger/object modeling, or complex control methods.
Proceedings ArticleDOI

Dynamic in-hand sliding manipulation

TL;DR: This paper presents a framework for planning the motion of an n-fingered robot hand to create an inertial load on a grasped object to achieve a desired in-grasp sliding motion based on a soft-finger limit surface contact model at each fingertip.
References
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Book

Applied Nonlinear Control

TL;DR: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
Journal ArticleDOI

A new model for control of systems with friction

TL;DR: A new dynamic model for friction is proposed that captures most of the friction behavior that has been observed experimentally, including the Stribeck effect, hysteresis, spring-like characteristics for stiction, and varying break-away force.
Journal ArticleDOI

Friction Models and Friction Compensation

TL;DR: This paper reviews friction phenomena and friction models of interest for automatic control, with particular emphasis given to two recently developed dynamic friction models: the Bliman-Sorine model and the LuGre model.
Journal ArticleDOI

Data-Driven Grasp Synthesis—A Survey

TL;DR: A review of the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps and an overview of the different methodologies are provided, which draw a parallel to the classical approaches that rely on analytic formulations.
Proceedings ArticleDOI

An overview of dexterous manipulation

TL;DR: An overview of research in dexterous manipulation is presented, which includes grasp planning and quality measures, and looks at mid- and low-level control frameworks, and then compares manipulation versus exploration.
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