Abstract-Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of NI-to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.

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The dynamic-systems approach to robotpathplanningdefinesa dynamics ofrbotbehavior in which task constraints contribute independently to a nonlinear vector field that governs robot actions. We address problems that arise in scaling this approach to handle complex behavioral requirements. We propose a dynamics that operates in the space of task constraints, determining the relative contribution of each constraint to the behavioral dynamics. Competition among task constraints is able to deal with problems that arise when combining constraint contributions, making it possible to specify tasks that are mome complex than simple navigation. To demonstrate the utility of this approach, we design a system of two agents to perform a cooperative navigation task We show how competition among constraints enables agents to make decisions regarding which behavior to execute in a given situation, resulting in the execution of sequences of behaviors that satisfy task requirements. We discuss the scalability of the competitive-dynamics approach to the design of more complex autonomous systems.

Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous and autonomous properties of biological evolution. The evaluation, selection and reproduction are carried out by and between the robots, without any need for human intervention. In this paper we propose a biologically inspired embodied evolution framework, which fully integrates self-preservation, recharging from external batteries in the environment, and self-reproduction, pair-wise exchange of genetic material, into a survival system. The individuals are, explicitly, evaluated for the performance of the battery capturing task, but also, implicitly, for the mating task by the fact that an individual that mates frequently has larger probability to spread its gene in the population. We have evaluated our method in simulation experiments and the simulation results show that the solutions obtained by our embodied evolution method were able to optimize the two survival tasks, battery capturing and mating, simultaneously. We have also performed preliminary experiments in hardware, with promising results.

Robotic simulation systems allow researchers, engineers, and students to test control algorithms in a safe environment, hut until recently these systems only simulated the dynamics of the mechanism itself and could not simulate (complex) contacts with other bodies in the environment However, if the robot's task involves grasping an object, accurate simulation of contact and friction forces is a necessity. Recently developed methods formulate the constrains as a linear complementarity problem, allowing a solution to he computed using proven algorithms, hut for anyone implementing such a system, several additional . considerations must he taken into account. In this paper we present the implementation of the dynamics module of our freely available grasping simulator and present an example grasping task.

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Abslracl-In recent years, a number of \,isual servo control slgarithms have been proposed. Most approaches try to solve the inherent problems of image-based and positionbased servoing by partitioning the eontml between image and Cartesian spaces. However, partitioning of the cuntml often causes the Cartesian path to become more complex, which might result in operation close to the joint limits. A solution to avoid the joint limits is to use a shortest-path approach, which avoids the limits in must cases.In this paper, two new shortest-path approaches to visual servoing are presented. Fin6 a pusition-baed approach b pmposed that guarantees bolh shortest Cartesian trajectory and object visibility. Then, a variant Is presented, which avoids the use of a 3D model of the target object by using homography based partial pose estimation.