Topic: estimation, prediction Oral presentation or poster presentation Home environments are envisioned as one of the key application areas for service robots. Robots operating in such environments are typically faced with a variety objects they have to deal with or to manipulate to fulfill a given task. Many objects are not rigid since they have moving parts such as drawers or doors. Understanding the spatial movements of parts of such objects is essential for service robots to allow them to plan relevant actions such as door-opening trajectories. Ideally, robots are able to autonomously infer these articulation models by observation. In this work, we therefore investigate the problem of learning kinematic models of articulated objects from observations. As an illustrating example, consider the left three images of Figure 1 which depict two examples for observations of the door of a microwave oven and a learned, one-dimensional description of the door motion. Our problem can be formulated as follows: Given a sequence of rigid body poses from observed objects parts, learn a compact kinematic model describing the whole articulated object. This kinematic model has to define (i) which parts are connected, (ii) the dimensionality of the latent (not observed) actuation space of the object, and (iii) a kinematic function between different body parts in a generative way allowing a robot

Abstract — Truly autonomous systems require the ability to monitor and adapt their internal body scheme throughout their entire lifetime. In this paper, we present an approach allowing a robot to learn from scratch and maintain a generative model of its own physical body through self-observation with a single monocular camera. We represent the robot’s internal model as a compact Bayesian network, consisting of local models that describe the physical relationships between neighboring body parts. We introduce a flexible Bayesian framework that allows to simultaneously select the maximum-likely network structure and to learn the underlying conditional density functions. Changes in the robot’s physiology can be detected by identifying mismatches between model predictions and the self-perception. To quickly adapt the model to changed situations, we developed an efficient search heuristic that starts from the structure of the best explaining memorized network and then replaces local components where necessary. In experiments carried out with a real robot equipped with a 6-DOF manipulator as well as in simulation, we show that our system can quickly adapt to changes of the body physiology in full 3D space, in particular with limited visibility, noisy and partially missing observations, and without the need for proprioception. I.

Abstract Commonly, the inverse kinematic function of robotic manipulators is derived analytically from the robot model. However, there are cases in which a model is not a priori available. In this paper, we propose an approach that enables an autonomous robot to estimate the inverse kinematic function on-the-fly directly from self-observation and without a given kinematic model. The robot executes randomly sampled joint configurations and observes the resulting world positions. To approximate the inverse kinematic function, we propose to use instance-based learning techniques such as Nearest Neighbor and Linear Weighted Regression. After learning, the robot can take advantage of the learned model to build roadmaps for motion planning. A further advantage of our approach is that the environment can implicitly be represented by the sample configurations. We analyze properties of this approach and present results obtained from experiments on a real 6-DOF robot and from simulation. We show that our approach allows us to accurately control robots with unknown kinematic models of various complexity and joint types. 1

Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the correspondinglinks. Ourmethodusesamixtureofparameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistichome environment settings.

the date of receipt and acceptance should be inserted later Abstract Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.