Haptic exploration of unknown objects is of great importance for acquiring multi-modal object representations, which enable a humanoid robot to autonomously execute grasping and manipulation tasks. In this paper we present a tactile exploration strategy to guide an anthropomorphic five-finger hand along the surface of previously unknown objects and build a 3D object representation based on acquired tactile point clouds. The proposed strategy makes use of the dynamic potential field approach suggested in the context of mobile robot navigation. To demonstrate the capabilities of this strategy, we conduct experiments in a detailed physics simulation using a model of the five-finger hand. Exploration results of several test objects are given.

In this paper, we address the problem of tactile exploration and subsequent extraction of grasp hypotheses for unknown objects with a multi-fingered anthropomorphic robot hand. We present extensions on our tactile exploration strategy for unknown objects based on a dynamic potential field approach resulting in selective exploration in regions of interest. In the subsequent feature extraction, faces found in the object model are considered to generate grasp affordances. Candidate grasps are validated in a four stage filtering pipeline to eliminate impossible grasps. To evaluate our approach, experiments were carried out in a detailed physics simulation using models of the five-finger hand and the test objects.

Abstract-While humans can manipulate deformable objects smoothly and naturally, this is still a challenge for autonomous robots due to the complex object dynamics. The presence of rigid environment constraints and altering contact phases between the deformable object, the manipulator, and the environment makes this problem even more challenging. This paper presents a framework for deformable object manipulation that makes use of a single human demonstration of the task. The recorded trajectories are automatically segmented into a sequence of haptic control primitives involving contact with the rigid environment and vision-guided grasp primitives. The recorded motion/force trajectories serve as reference for a compliant control scheme in contact situations. In order to cope with positioning uncertainties a variable admittance control is proposed. The proposed approach is validated in an experimental mounting task for a deformable linear object with multiple re-grasping. The task is demonstrated with a multimodal teleoperation system and transfered to a robotic platform with a pair of seven degrees of freedom manipulators.

This paper presents a novel robotic architecture that is suitable for modular distributed multi-robot systems. The architecture is based on an interface supporting real-time inter-process communication, which allows simple and highly efficient data exchange between the modules. It allows monitoring of the internal system state and easy logging, thus facilitating the module development. The extension to distributed systems is provided through a communication middleware, which enables fast and transparent exchange of data through the network, although without real-time guarantees. The advantages and disadvantages of the architecture are rated using an existing framework for evaluation of robot architectures.

SUMMARYRobotic systems operating in the real-world have to cope with unforeseen events by determining appropriate decisions based on noisy or partial knowledge. In this respect high functional robots are equipped with many sensors and actuators and run multiple processing modules in parallel. The resulting complexity is even further increased in case of cooperative multi-robot systems, since mechanisms for joint operation are needed. In this paper a complete and modular framework that handles this complexity in multi-robot systems is presented. It provides efficient exchange of generated data as well as a generic scheme for task execution and robot coordination.