I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

INTRODUCTION: Brief History. Multifingered Hands and Dextrous Manipulation. Outline of the Book. Bibliography. RIGID BODY MOTION: Rigid Body Transformations. Rotational Motion in R3. Rigid Motion in R3. Velocity of a Rigid Body. Wrenches and Reciprocal Screws. MANIPULATOR KINEMATICS: Introduction. Forward Kinematics. Inverse Kinematics. The Manipulator Jacobian. Redundant and Parallel Manipulators. ROBOT DYNAMICS AND CONTROL: Introduction. Lagrange's Equations. Dynamics of Open-Chain Manipulators. Lyapunov Stability Theory. Position Control and Trajectory Tracking. Control of Constrained Manipulators. MULTIFINGERED HAND KINEMATICS: Introduction to Grasping. Grasp Statics. Force-Closure. Grasp Planning. Grasp Constraints. Rolling Contact Kinematics. HAND DYNAMICS AND CONTROL: Lagrange's Equations with Constraints. Robot Hand Dynamics. Redundant and Nonmanipulable Robot Systems. Kinematics and Statics of Tendon Actuation. Control of Robot Hands. NONHOLONOMIC BEHAVIOR IN ROBOTIC SYSTEMS: Introduction. Controllability and Frobenius' Theorem. Examples of Nonholonomic Systems. Structure of Nonholonomic Systems. NONHOLONOMIC MOTION PLANNING: Introduction. Steering Model Control Systems Using Sinusoids. General Methods for Steering. Dynamic Finger Repositioning. FUTURE PROSPECTS: Robots in Hazardous Environments. Medical Applications for Multifingered Hands. Robots on a Small Scale: Microrobotics. APPENDICES: Lie Groups and Robot Kinematics. A Mathematica Package for Screw Calculus. Bibliography. Index Each chapter also includes a Summary, Bibliography, and Exercises

http://www.cds.caltech.edu/%7Emurray/books/MLS/pdf/mls94-complete.pdf

A Mathematical Introduction to Robotic Manipulation

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.

/pdf/flocking-for-multi-agent-dynamic-systems-algorithms-and-5efqu9aidp.pdf

Flocking for multi-agent dynamic systems: algorithms and theory

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.

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Design, fabrication and control of soft robots

Here, we present insights into human contact-control strategies by defining conditions to determine whether a human controls a contact state, empirically analyzing object-to-environment contact geometry data obtained from human demonstrations in a haptic simulation environment, and testing hypothesess about underlying human contact-control strategies. Using haptic demonstration data from eleven subjects who inserted non-convex objects into occluded holes, we tested the following human contact-control hypotheses: (h1) humans follow a task trajectory that tracks pre-planned contact-state waypoints organized in a contact-state graph (contact-waypoint hypothesis); (h2) humans traverse the contact-state graph, explicitly controlling some contact states or subsets of contact states, in addition to the pre-determined initial and final goal states (controlled subgraph hypothesis); (h3) humans use a control policy where the only controlled states are the starting state for the task and the goal state (state policy hypothesis). Notably, we found that humans tend to visit a select few contact states once they enter each state’s vicinity in the graph, which is evidence against h3. Yet humans do not always visit said states (visit probability \(<40\%\)), which is, in addition, evidence against h1 provided different humans adopt similar strategies. We show that a classifier to determine when humans control their trajectories to visit specific contact states, when parameterized correctly, is invariant to graph aggregation operations across the false-positive to false-negative tradeoff spectrum. This indicates our results are robust given the data we obtained and suggests that efforts to characterize human motion should focus on h2.

Experimental Analysis of Human Control Strategies in Contact Manipulation Tasks

The human selection of specific postures among the infinity of possibilities is the result of a long and complex process of learning. Through learning, humans seem to come to discover the properties of their bodies and how best to put them to use when performing a task. Exploiting the body’s kinematic characteristics, humans effectively use the body’s mechanical advantage to improve the transmission of the muscles’ tension into the forces the task requires. However, the efficiency of this transmission is also affected by the human muscle actuation physiology and dynamics. By also adjusting the body configurations to maximize this transmission of muscle tensions to resulting task forces, humans are in fact exploiting what can be termed the physiomechanical advantage of their musculoskeletal system. Here, we investigate the physiomechanical advantage of humans through several experimental validations. Based on the results of the analysis, we conclude that in learned tasks the optimization of the physiomechanical advantage corresponds to the overall minimization of the human muscular effort. The approach presented here can be applied for the motion control of human musculoskeletal models where the control is task-driven and the task consistent postures are driven by the muscular criteria.

Muscular Effort for the Characterization of Human Postural Behaviors

Technology for coring from a low-mass rover has been developed to enable core sample acquisition where a planetary rover experiences moderate slip during the coring operation. A new stereo vision technique, Absolute Motion Visual Odometry, is used to measure rover slip during coring and the slip is accommodated through corresponding arm pose updating. Coring rate is controlled by feedback of the measured force of the coring tool against the environment. Test results in the JPL Marsyard show that coring from a low-mass rover with slip is feasible.

/pdf/results-of-coring-from-a-low-mass-rover-4fu4o9af6u.pdf

Results of Coring from a Low Mass Rover

We present a light, actuated smart staff with 5DOF tip force sensing which can be used by a humanoid robot operating in challenging terrain. The staff has an extension mechanism that employs mechanical multiplexing to achieve a high extension ratio in a stiff and compact package. The tip force sensor uses two metal diaphragms to achieve decoupling of axial and radial forces and the ability to tune the maximum range of forces in each direction independently. With the force sensor, the robot can characterize the coefficient of friction and orientation of a surface with simple motion primitives. Two sets of experiments were conducted with the smart staff manipulated by a 7 DOF robot arm. Using the sensor, the robot was able to determine the orientations of sloped surfaces within two degrees in two orthogonal directions.

SupraPeds: Smart staff design and terrain characterization

Modeling simultaneous contact and collisions at multiple surfaces in articulated rigid-body systems is computationally challenging because joint and contact constraints must be enforced together. Previously, we have proposed an efficient method to treat multi-surface collisions in the contact space coordinates which represent the relative motion at the contact surfaces [12]. The inertial dynamics of the multi-surface collision is then equivalent to a simultaneous collision between two rigid bodies whose inertia equals the effective inertia of the respective articulated bodies in the contact space. In this paper, we present an experimental study of this model on planar and spatial systems of passive jointed pendulums. Across several frictionless/frictional, single/multi-surface collision events, the model accurately predicted post-collision joint positions and velocities. Furthermore, joint velocity predictions in the dynamically consistent null space were significantly more accurate than those in the contact space. This indicates that the contact space model accurately describes inertial dynamics of multi-surface collisions in articulated rigid-body systems, but a better empirical restitution model can further improve prediction accuracy .

Oussama Khatib

Papers

Experimental Analysis of Human Control Strategies in Contact Manipulation Tasks

Muscular Effort for the Characterization of Human Postural Behaviors

Results of Coring from a Low Mass Rover

SupraPeds: Smart staff design and terrain characterization

Experimental Studies of Contact Space Model for Multi-surface Collisions in Articulated Rigid-Body Systems