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

Task defined grasp force solutions

Journal ArticleForce Control for dextrous manipulation has been approached algebraically with a great deal of success, however, the computational burden created when such approaches are applied to grasps consisting of many contacts is prohibitive. This paper describes a procedure which restricts the complexity of the algebraic system of equations, and makes use of mathematical programming techniques to select a solution which is optimal with respect to an objective function. The solution is constrained by contact surface friction properties and the kinematic limitations of the hand. The application of the procedure to the selection of minimal internal grasp forces which allow the application of task defined external forces is described. Examples of the procedure are presented

High-level planning for dextrous manipulation

Journal ArticleThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably anthropomorphic manipulators to perform the deft movement that we take for granted in the human hand. The objective of this paper is the creation of a framework within which constraints involving the manipulator, the object, and the hand/object interaction can be exploited to direct a goal oriented manipulation. The analysis here is targeted for the Utah/MIT dextrous manipulator, but will support any general purpose dextrous manipulation system?

Apparent symmetries in range data

technical reportA procedure for extracting symmetrical features from the output of a range scanner is described which is insensitive to sensor noise and robust with respect to object surface complexity. The acquisition of symmetry descriptors for rigid bodies from a range image was in this case motivated by the need to direct pre-grasp configurations in dextrous manipulation systems. However, object symmetries are powerful features for object identification/matching and correspond explicitly to useful geometric object models such as generalized cylinder representations?

A survey of dextrous manipulation

technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1

A control paradigm for general purpose manipulation systems

Journal ArticleMechanical end effectors capable of dextrous manipulation are now a reality. Solutions to the high level control issues, however, have so far proved difficult to formulate. We propose a methodology for control which produces the functionality required for a general purpose manipulation system. It is clear that the state of a hand/object system is a complex interaction between the geometry of the object, the character of the contact interaction, and the conditioning of the manipulator. The objective of this work is the creation of a framework within which constraints involving the manipulator, the object, and the hand/object interaction can be exploited to direct a goal oriented manipulation strategy. The set of contacts that are applied to a task can be partitioned into subsets with independent objectives. The individual contacts may then be driven over the interaction surface to improve the state of the grasp while the configuration of the hand addresses the application of required forces. A system of this sort is flexible enough to manage large numbers of contacts and to address manipulation tasks which require the removal and replacement of fingers in the grasp. A simulator has been constructed and results of its application to position synthesis for initial grasps is presented. A discussion of the manipulation testbed under construction at the University of Utah employing the Utah/MIT Dextrous hand is presented

Improving Grasp Skills Using Schema Structured Learning

Abstract In the control-based approach to robotics, complex behavior is created by sequencing and combining control primitives. While it is desirable for the robot to autonomously learn the correct control sequence, searching through the large number of potential solutions can be time consuming. This paper constrains this search to variations of a generalized solution encoded in a framework known as an action schema. A new algorithm, SCHEMA STRUCTURED LEARNING, is proposed that repeatedly executes variations of the generalized solution in search of instantiations that satisfy action schema objectives. This approach is tested in a grasping task where Dexter, the UMass humanoid robot, learns which reaching and grasping controllers maximize the probability of grasp success

Sensor-Based Contact Geometry Optimization for Multifingered Robot Hands

This paper employs a behavior-based approach to regulate the contact geometry during a dextrous (fingertip) grasp. The goal is to provide a framework for sensor-based controllers that acquire information on-line and use this data stream to refine grasp solutions. We will present two such behaviors and illustrates how they can be used to suppress local errors in the neighborhood of a grasp solution. We present a class of objects for which our behavioral repertoire is globally competent for 2, 3, and 4 contacts, and report the performance of the grasp regulator on this class of objects. This work is part of a larger treatment for a 20 degree of freedom (DOF) eye/hand/arm system under development at the Laboratory for Perceptual Robotics. Introduction Success in dextrous manipulation tasks, as in any other real-time task involving interaction with the environment, implies considering perception and action as integral parts of the controller. Integrating perception and action in dextrous..