Grasp planning under uncertainty

The planning of dexterous grasps for multifingered robot hands operating in uncertain environments is covered. A sensor-based approach to the planning of a reach path prior to grasping is first described. An on-line, joint space finger path planning algorithm for the enclose phase of grasping was then developed. The algorithm minimizes the impact momentum of the hand. It uses a Preshape Jacobian matrix to map task-level hand preshape requirements into kinematic constraints. A master slave scheme avoids inter-finger collisions and reduces the dimensionality of the planning problem

Scale-Dependent Grasp

This paper discusses the scale-dependent grasp.Suppose that a human approaches an object initially placed on atable and finally achieves an enveloping grasp. Under such initialand final conditions, he (or she) unconsciously changes the graspstrategy according to the size of objects, even though they havesimilar geometry. We call the grasp planning the scale-dependentgrasp. We find that grasp patterns are also changed according tothe surface friction and the geometry of cross section in additionto the scale of object. Focusing on column objects, we first classifythe grasp patterns and extract the essential motions so that we canconstruct grasp strategies applicable to multifingered robot hands.The grasp strategies constructed for robot hands are verified byexperiments. We also consider how a robot hand can recognizethe failure mode and how it can switch from one to another

Bio-Inspired Motion Strategies for a Bimanual Manipulation Task

Steffen JF, Elbrechter C, Haschke R, Ritter H. Bio-Inspired Motion Strategies for a Bimanual Manipulation Task. In: International Conference on Humanoid Robots (Humanoids). 2010

Toward Dynamic Manipulation of Flexible Objects by High-Speed Robot System: From Static to Dynamic

This chapter explains dynamic manipulation of flexible objects, where the target objects to be manipulated include rope, ribbon, cloth, pizza dough, and so on. Previously, flexible object manipulation has been performed in a static or quasi-static state. Therefore, the manipulation time becomes long, and the efficiency of the manipulation is not considered to be sufficient. In order to solve these problems, we propose a novel control strategy and motion planning for achieving flexible object manipulation at high speed. The proposed strategy simplifies the flexible object dynamics. Moreover, we implemented a high-speed vision system and high-speed image processing to improve the success rate by manipulating the robot trajectory. By using this strategy, motion planning, and high-speed visual feedback, we demonstrated several tasks, including dynamic manipulation and knotting of a rope, generating a ribbon shape, dynamic folding of cloth, rope insertion, and pizza dough rotation, and we show experimental results obtained by using the high-speed robot system

HEAP: A Sensory Driven Distributed Manipulation System

We address the problems of locating, grasping, and removing one or more unknown objects from a given area. In order to accomplish the task we use HEAP, a system of coordinating the motions of the hand and arm. HEAP also includes a laser range finer, mounted at the end of a PUMA 560, allowing the system to obtain multiple views of the workspace. We obtain volumetric information of the objects we locate by fitting superquadric surfaces on the raw range data. The volumetric information is used to ascertain the best hand configuration to enclose and constrain the object stably. The Penn Hand used to grasp the object, is fitted with 14 tactile sensors to determine the contact area and the normal components of the grasping forces. In addition the hand is used as a sensor to avoid any undesired collisions. The objective in grasping the objects is not to impart arbitrary forces on the object, but instead to be able to grasp a variety of objects using a simple grasping scheme assisted with a volumetric description and force and touch sensing

Haptic Perception with a Robot Hand: Requirements and Realization

This paper first discusses briefly some of the recent ideas of perceptual psychology on the human haptic system particularly those of J.J. Gibson and Klatzky and Lederman. Following this introduction, we present some of the requirements of robotic haptic sensing and the results of experiments using a Utah/MIT dexterous robot hand to derive geometric object information using active sensing

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab