This paper formalises Object-Action Complexes (OACs) as a basis for symbolic representations of sensorimotor experience and behaviours. OACs are designed to capture the interaction between objects and associated actions in artificial cognitive systems. This paper gives a formal definition of OACs, provides examples of their use for autonomous cognitive robots, and enumerates a number of critical learning problems in terms of OACs.

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The exploration and learning of new objects is an essential capability of a cognitive robot. In this paper we focus on making use of the robot's manipulation abilities to learn complete object representations suitable for 3D object recognition. Taking control of the object allows the robot to focus on relevant parts of the images, thus bypassing potential pitfalls of purely bottom-up attention and segmentation. The main contribution of the paper consists in integrated visuomotor processes that allow the robot to learn object representations by manipulation without having any prior knowledge about the 267 268 A. Ude, D. Omrčen & G. Cheng objects. Our experimental results show that the acquired data is of sufficient quality to train a classifier that can recognize 3D objects independently of the viewpoint.

This paper investigates the extent to which biarticular actuation mechanisms—spring-driven redundant actuation schemes that extend over two joints, similar in function to biarticular muscles found in legged animals—improve the performance of jumping and other fast explosive robot movements. Robust numerical optimization algorithms that take into account the complex dynamics of both the redundantly actuated system and frictional contact forces are developed. We then quantitatively evaluate the gains in vertical jumping vis-à-vis monoarticular and biarticular joint actuation schemes and examine the effects of spring stiffness and activation angle on overall jump performance. Both numerical simulations and experiments with a hardware prototype of a biarticular legged robot are reported.

This paper deals with the stability of null-space velocity control algorithms in extended operational space for redundant robots. We compare the performance of the control algorithm based on the minimal null-space projection and generalized-inverse-based projection into the Jacobian nullspace. We show how the null-space projection affects the performance of the null-space tracking algorithm. The results are verified with the simulation and real implementation on a redundant mobile robot composed of 3 degrees of freedom (DOFs) mobile platform and 7-DOF robot arm.