This paper"'describes some preliminary work on dynamic manipulation--some ezamples, a definition, and analysis of throwing a club

In this paper, an attempt at summarizing the evolution and the state-of-the-art in the field of robot hands is made. In such exposition, a critical evaluation of what in the author's view are the leading ideas and emerging trends, is privileged with respect to exhaustiveness of citations. The survey is focused mainly on three types of functional requirements a machine hand can be assigned in an artificial system, namely, manipulative dexterity, grasp robustness, and human operability. A basic distinction is made between hands designed for mimicking the human anatomy and physiology, and hands designed to meet restricted, practical requirements. In the latter domain, arguments are presented in favor of a "minimalistic" attitude in the design of hands for practical applications, i.e., use the least number of actuators, the simplest set of sensors, etc., for a given task. To achieve this rather obvious engineering goal is a challenge to our community. The paper illustrates some of the ...

biped robots

Captured human motion data can provide a rich source of examples of successful manipulation strategies. General techniques for adapting these examples for use in robotics are not yet available, however, in part because the problem to be solved by the robot will rarely be the same as that in the human demonstration. This paper considers the problem of adapting a human demonstration of a quasistatic manipulation task to new objects and friction conditions (Figure 1). We argue that a manipulation plan is similar to a demonstration if it involves the identical number of contacts and if the applied contact wrenches follow similar trajectories. Based on this notion of similarity, we present an algorithm that uses the human demonstration to constrain the solution space to a set of manipulation plans similar to the demonstration. Our algorithm provides guarantees on maximum task forces and flexibility in contact placement. Results for the task of tumbling large, heavy objects show that manipulation plans similar to a demonstration can be synthesized for a variety of object sizes, shapes, and coe#cients of friction. Experimental results with a humanoid robot show that the approach produces natural-looking motion in addition to e#ective manipulation plans.

This note investigates the minimal number of unilateral thrusters required for different versions of nonlinear controllability of a planar rigid body. For one to three unilateral thrusters, we get a new property with each additional thruster: one thruster yields small-time accessibility on the body's state space TSE(2); two thrusters yield global controllability on TSE(2); and three thrusters yield small-time local controllability at zero velocity states.

This paper applies the idea of forced recurrence to demonstrate controllability and stabilizability of a single-input juggling system. Nonlinear optimization is used to find controls in a neighborhood of the recurrent controls that drive the system toward the goal trajectory. The approach is demonstrated on an experimental juggling system. Index Terms---Global controllability, juggling, nonlinear optimization, recurrence, robotic manipulation. I.

In this paper we introduce a newly developed high-speed multi-fingered robotic hand. The hand has 8joints and 3-fingers. A newly developed small harmonic drive gear and a high-power mini actuator are fitted in each finger link, and a strain gauge sensor is in each joint. The weight of the hand module is only 0.8kg, but high-speed motion and high-power grasping are possible. The hand can close its joints at 180deg per 0.1s, and the fingertips have an output force of about 28N. The hand system is controlled by a massively parallel vision system. Experimental results are shown in which a falling object was caught by the highspeed hand.

This paper outlines geometric and algorithmic issues common to various types of nonprehensile manipulation and gives some results for planar dynamic manipulation. 1 Overview Nonprehensile manipulation is manipulation without a form- or force-closure grasp. Examples include pushing, throwing, juggling, tapping, batting, and rolling (Mason [24]; Higuchi [11]; Buhler and Koditschek [6]; Erdmann [8]; Huang et al. [12]; Zumel and Erdmann [40]; Aiyama et al. [1]; Trinkle and Zeng [37]). In each of these examples, the robot takes advantage of the natural task dynamics to help control the motion of the part. Nonprehensile manipulation occupies the majority of the manipulation spectrum, comprising everything between situations where the robot exerts complete control to situations where the natural dynamics exert complete control. During a baseball throw, the ball is at first held firmly in the hand, then is allowed to roll off the fingers, and finally follows a free-flight trajectory determined by gravity and air resistance. The nonprehensile manipulation problem is to arrange the rolling motion on the fingers such that the release state will allow the ball to reach the goal state

We address the manipulation of planar objects by multiple cooperating mobile robots using the concept of Object Closure. In contrast to Form or Force Closure, Object Closure is a condition under which the object is trapped so that there is no feasible path for the object from the given position to any position that is beyond a specified threshold distance. Once Object Closure is achieved, the robots can cooperatively drag or flow the trapped object to the desired goal. In this paper, we define object closure and develop a set of decentralized algorithms that allow the robots to achieve and maintain object closure. We show how simple, first-order, potential field based controllers can be used to implement multirobot manipulation tasks.

Abstract — We introduce a learning-based approach to manipulation in unstructured environments. This approach permits autonomous acquisition of manipulation expertise from interactions with the environment. The resulting expertise enables a robot to perform effective manipulation based on partial state information. The manipulation expertise is represented in a relational state representation and learned using relational reinforcement learning. The relational representation renders learning tractable by collapsing a large number of states onto a single, relational state. The relational state representation is carefully grounded in the perceptual and interaction skills of the robot. This ensures that symbolically learned knowledge remains meaningful in the physical world. We experimentally validate the proposed learning approach on the task of manipulating an articulated object to obtain a model of its kinematic structure. Our experiments demonstrate that the manipulation expertise acquired by the robot leads to substantial performance improvements. These improvements are maintained when experience is applied to previously unseen objects. I.