The synthesis of multi-fingered grasps on nontrivial objects requires a realistic representation of the contact between the fingers of a robotic hand and an object. In this work, we use a patch contact model to approximate the contact between a rigid object and a deformable anthropomorphic finger. This contact model is utilized in the computation of Independent Contact Regions (ICRs) that have been proposed as a way to compensate for shortcomings in the finger positioning accuracy of robotic grasping devices. We extend the ICR algorithm to account for the patch contact model and show the benefits of this solution.

The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.

The concept of independent contact regions on a target object's surface, in order to compensate for shortcomings in the positioning accuracy of robotic grasping devices, is well known. However, the numbers and distributions of contact points forming such regions is not unique and depends on the underlying computational method. In this work we present a computation scheme allowing to prioritize contact points for inclusion in the independent regions. This enables a user to affect their shape in order to meet the demands of the targeted application. The introduced method utilizes frictionless contact constraints and is able to efficiently approximate the space of disturbances resistible by all grasps comprising contacts within the independent regions.

Robot skills can be taught and recognized by a Programming-by-Demonstration technique where first a human operator demonstrates a set of reference skills. The operator's motions are then recorded by a data-capturing system and modeled via fuzzy clustering and a Takagi-Sugeno modeling technique. The resulting skill models use the time as input and the operator's actions as outputs. During the recognition phase, the robot recognizes which skill has been used by the operator in a novel demonstration. This is done by comparison between the time clusters of the test skill and those of the reference skills. Finally, the robot executes the recognized skill by using the corresponding reference skill model. Drastic differences between learned and real world conditions which occur during the execution of skills by the robot are eliminated by using the Broyden update formula for Jacobians. This method was extended for fuzzy models especially for time cluster models. After the online training of a skill model the updated model is used for further executions of the same skill by the robot.

Since the introduction of independent contact regions in order to compensate for shortcomings in the positioning accuracy of robotic hands, alternative methods for their generation have been proposed. Due to the fact that (in general) such regions are not unique, the computation methods used usually reflect the envisioned application and/or underlying assumptions made. This paper introduces a parallelizable algorithm for the efficient computation of independent contact regions, under the assumption that a user input in the form of initial guess for the grasping points is readily available. The proposed approach works on discretized 3D-objects with any number of contacts and can be used with any of the following models: frictionless point contact, point contact with friction and soft finger contact. An example of the computation of independent contact regions comprising a non-trivial task wrench space is given.

Robot skills are motion or grasping primitives from which a complicated robot task consists. Skills can be directly learned and recognized by a technique named Programming-by-Demonstration. A human operator demonstrates a set of reference skills where his motions are recorded by a data-capturing system and modeled via fuzzy clustering and a Takagi-Sugeno modeling technique. The skill models use the time instants as input and the actions of the operator as outputs. In the recognition phase, the robot identifies the skill that has been shown by the operator in a novel test demonstration. The recognition is done by comparing of the time clusters of the test skill and the reference skills. Finally, using the corresponding reference skill model the robot executes the recognized skill. Skill models can be updated on-line where drastic differences between learned and real world conditions during the execution of skills by the robot are eliminated using the Broyden update formula. This method was extended for fuzzy models especially for time cluster models. The updated model is used for further executions of the same skill by the robot.

This paper deals with three different methods for grasp recognition for a human hand. Grasp recognition is a major part of the approach for programming-by-demonstration (PbD) for five-fingered robotic hands. A human operator instructs the robot to perform different grasps wearing a data glove. For a number of human grasps, the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using the time as input parameter and the joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are presented and compared. In the first method the test grasp is compared with model grasps using the difference between the model outputs. In the second one, qualitative fuzzy models are used for recognition and classification. The third method is based on hidden-Markov-models (HMM) which are commonly used in robot learning.

In this article we address the problem of programming by demonstration (PbD) of grasping tasks for a five-fingered robotic hand. The robot is instructed by a human operator wearing a data glove capturing the hand poses. For a number of human grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzy clustering and Takagi-Sugeno modeling. This so-called time-clustering leads to grasp models using the time as input parameter and the fingertip positions as outputs. For a test sequence of grasps the control system of the robot hand identifies the grasp segments, classifies the grasps and generates the sequence of grasps shown before. For this purpose, each grasp is correlated with a training sequence. By means of a hybrid fuzzy model the demonstrated grasp sequence can be reconstructed.

The focus of the paper is the learning of grasp primitives for a five-Angered anthropomorphic robotic hand via teaching-by-demonstration and fuzzy modeling. In this approach, a number of basic grasps is demonstrated by a human operator wearing a data glove which continuously captures the hand pose. The resulting fingertip trajectories and joint angles are clustered and modeled in time and space so that the motions of the fingers forming a particular grasp are modeled in a most effective and compact way. Classification and learning are based on fuzzy clustering and Takagi Sugeno (TS) modeling. The presented method allows to learn, imitate and recognize the motion sequences forming specific grasps.

Sliding mode control algorithms can provide high robustness with respect to bounded disturbances and parameter variations. However, the classical design procedure sets strong restrictions on the choice of a sliding surface. In this paper we propose a new algorithm which employs a Takagi-Sugeno fuzzy system to represent the sliding surface. It can provide nearly time-optimal behavior of the system and still retain the robustness properties.