Humanoid Mobile Manipulation Using Controller Refinement

An important class of mobile manipulation problems are move-to-grasp problems where a mobile robot must navigate to and pick up an object. One of the distinguishing features of this class of tasks is its coarse-to-fine structure. Near the beginning of the task, the robot can only sense the target object coarsely or indirectly and make gross motion toward the object. However, after the robot has located and approached the object, the robot must finely control its grasping contacts using precise visual and haptic feedback. In this paper, it is proposed that move-to-grasp problems are naturally solved by a sequence of controllers that iteratively refines what ultimately becomes the final solution. This paper introduces the notion of a refining sequence of controllers and characterizes this type of solution. The approach is demonstrated in a move-to-grasp task where Robonaut, the NASA/JSC dexterous humanoid, is mounted on a mobile base and navigates to and picks up a geological sample box. In a series of tests, it is shown that a refining sequence of controllers decreases variance in robot configuration relative to the sample box until a successful grasp has been achieved

Tactile Gloves for Autonomous Grasping With the NASA/DARPA Robonaut

Tactile data from rugged gloves are providing the foundation for developing autonomous grasping skills for the NASA/DARPA Robonaut, a dexterous humanoid robot. These custom gloves compliment the human like dexterity available in the Robonaut hands. Multiple versions of the gloves are discussed, showing a progression in using advanced materials and construction techniques to enhance sensitivity and overall sensor coverage. The force data provided by the gloves can be used to improve dexterous, tool and power grasping primitives. Experiments with the latest gloves focus on the use of tools, specifically a power drill used to approximate an astronaut's torque tool

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

Movement Speed Models of Natural Grasp and Release Used for an Industrial Robot Equipped with a Gripper

Abstract. In this paper, movement speed models of a robotic manipulator are presented according to the mode of operation of the human hand, when it wants to grasp and release an object. In order to develop the models, measurements on a human agent were required regarding the movement coordinates of his hand. The movement patterns have been approximated on the intervals, using first and second degree functions. The speeds were obtained by deriving these functions. The models obtained are generally presented; for their implementation in models applied for a certain robot, specific changes from case to case have to be made

Learning Grasp Strategies Composed of Contact Relative Motions

Of central importance to grasp synthesis algorithms are the assumptions made about the object to be grasped and the sensory information that is available. Many approaches avoid the issue of sensing entirely by assuming that complete information is available. In contrast, this paper proposes an approach to grasp synthesis expressed in terms of units of control that simultaneously change the contact configuration and sense information about the object and the relative manipulator-object pose. These units of control, known as contact relative motions (CRMs), allow the grasp synthesis problem to be recast as an optimal control problem where the goal is to find a strategy for executing CRMs that leads to a grasp in the shortest number of steps. An experiment is described that uses Robonaut, the NASA-JSC space humanoid, to show that CRMs are a viable means of synthesizing grasps. However, because of the limited amount of information that a single CRM can sense, the optimal control problem may be partially observable. This paper proposes expressing the problem as a k-order Markov Decision Process (MDP) and solving it using Reinforcement Learning. This approach is tested in a simulation of a two-contact manipulator that learns to grasp an object. Grasp strategies learned in simulation are tested on the physical Robonaut platform and found to lead to grasp configurations consistently

Control Techniques for Robot Manipulator Systems with Modeling Uncertainties

This dissertation describes the design and implementation of various nonlinear control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Chapter 2 describes the development of an adaptive task-space tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Experimental results for a planar application of the Barrett whole arm manipulator (WAM) are provided to illustrate the performance of the developed adaptive controller. The controller developed in Chapter 2 requires the assumption that the manipulator models are linearly parameterizable. However there might be scenarios where the structure of the manipulator dynamic model itself is unknown due to difficulty in modeling. One such example is the continuum or hyper-redundant robot manipulator. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In Chapter 3, a joint level controller for continuum robots is described which utilizes a neural network feedforward component to compensate for dynamic uncertainties. Experimental results are provided to illustrate that the addition of the neural network feedforward component to the controller provides improved tracking performance. While Chapter\u27s 2 and 3 described two different joint controllers for robot manipulators, in Chapter 4 a controller is developed for the specific task of whole arm grasping using a kinematically redundant robot manipulator. The whole arm grasping control problem is broken down into two steps; first, a kinematic level path planner is designed which facilitates the encoding of both the end-effector position as well as the manipulators self-motion positioning information as a desired trajectory for the manipulator joints. Then, the controller described in Chapter 3, which provides asymptotic tracking of the encoded desired joint trajectory in the presence of dynamic uncertainties is utilized. Experimental results using the Barrett Whole Arm Manipulator are presented to demonstrate the validity of the approach

Nonlinear Control Techniques for Robot Manipulators

This Masters thesis describes the design and implementation of control strategies for the following topics of research: i) Whole Arm Grasping Control for Redundant Robot Manipulators, ii) Neural Network Grasping Controller for Continuum Robots and, iii) Coordination Control for Haptic and Teleoperator Systems. An approach to whole arm grasping of objects using redundant robot manipulators is presented. A kinematic control which facilitates the encoding of both the end-effector position, as well as body self-motion positioning information as a desired trajectory signal for the manipulator joints is developed. An approach is presented to whole arm grasping control for continuum robots. The grasping controller is developed in two stages; high level path planning for the grasping objective, and a low level joint controller using a neural network feedforward component to compensate for dynamic uncertainties. Lastly, two controllers are developed for nonlinear haptic and teleoperator systems for coordination of the master and slave systems

Teaching a robot manipulation skills through demonstration

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (p. 127-129).An automated software system has been developed to allow robots to learn a generalized motor skill from demonstrations given by a human operator. Data is captured using a teleoperation suit as a task is performed repeatedly on Leonardo, the Robotic Life group's anthropomorphic robot, in different parts of his workspace. Stereo vision and tactile feedback data are also captured. Joint and end effector motions are measured through time, and an improved Mean Squared Velocity [MSV] analysis is performed to segment motions into possible goal-directed streams. Further combinatorial selection of subsets of markers allows final episodic boundary selection and time alignment of tasks. The task trials are then analyzed spatially using radial basis functions [RBFs] to interpolate demonstrations to span his workspace, using the object position as the motion blending parameter. An analysis of the motions in the object coordinate space [with the origin defined at the object] and absolute world-coordinate space [with the origin defined at the base of the robot], and motion variances in both coordinate frames, leads to a measure [referred to here as objectivity] of how much any part of an action is absolutely oriented, and how much is object-based. A secondary RBF solution, using end effector paths in the object coordinate frame, provides precise end-effector positioning relative to the object. The objectivity measure is used to blend between these two solutions, using the initial RBF solution to preserve quality of motion, and the secondary end-effector objective RBF solution to increase the robot's capability to engage objects accurately and robustly.by Jeff Lieberman.S.M