We study the problem of actively searching for an object in a three-dimensional (3-D) environment under the constraint of a maximum search time using a visually guided humanoid robot with 26 degrees of freedom. The inherent intractability of the problem is discussed, and a greedy strategy for selecting the best next viewpoint is employed. We describe a target probability updating scheme approximating the optimal solution to the problem, providing an efficient solution to the selection of the best next viewpoint. We employ a hierarchical recognition architecture, inspired by human vision, that uses contextual cues for attending to the view-tuned units at the proper intrinsic scales and for active control of the robotic platform sensor's coordinate frame, which also gives us control of the extrinsic image scale and achieves the proper sequence of pathognomonic views of the scene. The recognition model makes no particular assumptions on shape properties like texture and is trained by showing the object by hand to the robot. Our results demonstrate the feasibility of using state-of-the-art vision-based systems for efficient and reliable object localization in an indoor 3-D environment.

Reactive control and planning are complementary methods in robot motion control. The advantage of planning is the ability to find difficult solutions, optimize trajectories globally and not getting stuck in local minima but at higher computational cost. On the other hand, reactive control can handle dynamic or uncertain environments at low computational cost, but may get stuck in local minima.

This paper presents a decentralized planning method for generating dynamic whole body motions of multilink robots including humanoids. First, a robotic system will be modeled as a general multi-body dynamical system. The planning problem of a multi-body system will then be formulated as a constraint resolution problem. The problem will be solved by means of an extended Gauss-Seidel method, which is capable of handling multiple constraint groups with different priorities. The method will be demonstrated in whole-body motion generation tasks of a humanoid, both in numerical simulations and in experiments using a real humanoid robot.

Based on inspirations from infant development we present a system which learns associations between acoustic labels and visual representations in interaction with its tutor. The system is integrated with a humanoid robot. Except for a few trigger phrases to start learning all acoustical representations are learned online and in interaction. Similar, for the visual domain the clusters are not predefined and fully learned online. In contrast to other interactive systems the interaction with the acoustic environment is solely based on the two microphones mounted on the robots head. In this paper we give an overview on all key elements of the system and focus on the challenges arising from the headset-free learning of speech labels. In particular we present a mechanism for auditory attention integrating bottom-up and top-down information for the segmentation of the acoustic stream. The performance of the system is evaluated based on offline tests of individual parts of the system and an analysis of the online behavior.

We describe a system for visual interaction developed for humanoid robots. It enables the robot to interact with its environment using a smooth whole body motion control driven by stabilized visual targets. Targets are defined as visually extracted "proto-objects" and behavior-relevant object hypotheses and are stabilized by means of a short-term sensory memory. Selection mechanisms are used to switch between behavior alternatives for searching or tracking objects as well as different whole body motion strategies for reaching. The decision between different motion strategies like reaching with right or left hand or with and without walking is made based on internal predictions that use copies of the whole-body control algorithm. The results show robust object tracking and a smooth interaction behavior that includes a large variety of whole-body postures.

We propose a self collision avoidance system that superposes trajectories in order not only to protect the robot's hardware but also to enable continuous motions. The system runs in real-time so that the robot can work in an uncertain environment. It is based on virtual forces between close segments of the robot. The avoidance movements are blended with a whole body motion control in order to change the priority between target reaching and collision avoidance. The blending is performed autonomously without the necessity of external switching. Our method works both while the robot is standing and walking. Reaching motions from the front to the side of the body without the arm colliding with the body are possible. Even if the target is inside the body, the arm stops at the closest point to the target outside of the body. Our method can be used for other applications: We apply it to realizing a "body schema" and for "occlusion avoidance."

This paper presents a whole body motion algorithm and shows some steps towards its feasibility in complex scenarios. We employ the framework of Liegeois, (1977) which solves the redundant inverse kinematics problem on velocity level. To make the controller suitable for a variety of different applications, task descriptors for the relative effector positions as well as a one-and two-dimensional attitude representation are proposed. The inverse kinematics are extended by allowing for "displacement intervals" which are formulated in task space. The proposed control scheme guarantees that the effector motion lies within the specified interval. However, the motion inside the interval is determined by optimization criteria, which can effectively be utilized to generate a more flexible and robust motion. We discuss an example and show simulation and experimental results on the humanoid robot ASIMO

Proposes a framework of a neural information processing architecture. The authors attempt to extract the basic principles and operations typical for neural information processing and describe a hierarchical extendible system for the problem of exploration and orientation in a natural environment as an example for an ill-posed and highly complex problem. Realisations of the main elements of this processing structure are discussed.>