Nikolaus Vahrenkamp

ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

In this paper, we present a new humanoid robot currently being developed for applications in human-centered environments. In order for humanoid robots to enter human-centered environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans. The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario. We introduce the different subsystems of the robot. We present the kinematics, sensors, and the hardware and software architecture. We propose a hierarchically organized architecture and introduce the mapping of the functional features in this architecture into hardware and software modules. We also describe different skills related to real-time object localization and motor control, which have been realized and integrated into the entire control architecture

Humanoid motion planning for dual-arm manipulation and re-grasping tasks

In this paper, we present efficient solutions for planning motions of dual-arm manipulation and re-grasping tasks. Motion planning for such tasks on humanoid robots with a high number of degrees of freedom (DoF) requires computationally efficient approaches to determine the robots full joint configuration at a given grasping position, i.e. solving the Inverse Kinematics (IK) problem for one or both hands of the robot. In this context, we investigate solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robots pre-computed reachability space with random sampling of free parameters. This strategy provides feasible IK solutions at a low computation cost without resorting to iterative methods which could be trapped by joint-limits. We apply this strategy to dual-arm motion planning tasks in which the robot is holding an object with one hand in order to generate whole-body robot configurations suitable for grasping the object with both hands. In addition, we present two probabilistically complete RRT-based motion planning algorithms (J+-RRT and IK-RRT) that interleave the search for an IK solution with the search for a collision-free trajectory and the extension of these planners to solving re-grasping problems. The capabilities of combining IK methods and planners are shown both in simulation and on the humanoid robot ARMAR-III performing dual-arm tasks in a kitchen environment.

Toward humanoid manipulation in human-centred environments

In order for humanoid robots to enter human-centred environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans. The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario. In this paper, we present a new humanoid robot currently being developed for applications in human-centred environments. In addition, we present an integrated grasping and manipulation system consisting of a motion planner for the generation of collision-free paths and a vision system for the recognition and localization of a subset of household objects as well as a grasp analysis component which provides the most feasible grasp configurations for each object.

Visual servoing for humanoid grasping and manipulation tasks

Using visual feedback to control the movement of the end-effector is a common approach for robust execution of robot movements in real-world scenarios. Over the years several visual servoing algorithms have been developed and implemented for various types of robot hardware. In this paper, we present a hybrid approach which combines visual estimations with kinematically determined orientations to control the movement of a humanoid arm. The approach has been evaluated with the humanoid robot ARMAR III using the stereo system of the active head for perception as well as the torso and arms equipped with five finger hands for actuation. We show how a robust visual perception is used to control complex robots without any hand-eye calibration. Furthermore, the robustness of the system is improved by estimating the hand position in case of failed visual hand tracking due to lightning artifacts or occlusions. The proposed control scheme is based on the fusion of the sensor channels for visual perception, force measurement and motor encoder data. The combination of these different data sources results in a reactive, visually guided control that allows the robot ARMAR-III to execute grasping tasks in a real-world scenario.

The KIT whole-body human motion database

We present a large-scale whole-body human motion database consisting of captured raw motion data as well as the corresponding post-processed motions. This database serves as a key element for a wide variety of research questions related e.g. to human motion analysis, imitation learning, action recognition and motion generation in robotics. In contrast to previous approaches, the motion data in our database considers the motions of the observed human subject as well as the objects with which the subject is interacting. The information about human-object relations is crucial for the proper understanding of human actions and their goal-directed reproduction on a robot. To facilitate the creation and processing of human motion data, we propose procedures and techniques for capturing of motion, labeling and organization of the motion capture data based on a Motion Description Tree, as well as for the normalization of human motion to an unified representation based on a reference model of the human body. We provide software tools and interfaces to the database allowing access and efficient search with the proposed motion representation.

Robot placement based on reachability inversion

Having a representation of the capabilities of a robot is helpful when online queries, such as solving the inverse kinematics (IK) problem for grasping tasks, must be processed efficiently in the real world. When workspace representations, e.g. the reachability of an arm, are considered, additional quality information such as manipulability or self-distance can be employed to enrich the spatial data. In this work we present an approach of inverting such precomputed reachability representations in order to generate suitable robot base positions for grasping. Compared to existing works, our approach is able to generate a distribution in SE(2), the cross-space consisting of 2D position and 1D orientation, that describes potential robot base poses together with a quality index. We show how this distribution can be queried quickly in order to find oriented base poses from which a target grasping pose is reachable without collisions. The approach is evaluated in simulation using the humanoid robot ARMAR-III [1] and an extension is presented that allows to find suitable base poses for trajectory execution.

Simultaneous Grasp and Motion Planning: Humanoid Robot ARMAR-III

In this work, we present an integrated approach for planning collision-free grasping motions. Therefore, rapidly exploring random tree (RRT)-based algorithms are used to build a tree of reachable and collision-free configurations. During tree generation, both grasp hypotheses and approach movements toward them are computed. The quality of reachable grasping poses is evaluated using grasp wrench space (GWS) analysis. We present an extension to a dual-arm planner that generates bimanual grasps together with collision-free dual-arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III (Figure 1).

Master Motor Map (MMM) — Framework and toolkit for capturing, representing, and reproducing human motion on humanoid robots

We present an extended version of our work on the design and implementation of a reference model of the human body, the Master Motor Map (MMM) which should serve as a unifying framework for capturing human motions, their representation in standard data structures and formats as well as their reproduction on humanoid robots. The MMM combines the definition of a comprehensive kinematics and dynamics model of the human body with 104 DoF including hands and feet with procedures and tools for unified capturing of human motions. We present online motion converters for the mapping of human and object motions to the MMM model while taking into account subject specific anthropométrie data as well as for the mapping of MMM motion to a target robot kinematics. Experimental evaluation of the approach performed on VICON motion recordings demonstrate the benefits of the MMM as an important step towards standardized human motion representation and mapping to humanoid robots.

Simox: A Robotics Toolbox for Simulation, Motion and Grasp Planning

Software development plays a major role besides hardware setup and mechanical design when it comes to building complex robots such as mobile manipulators or humanoids. Different requirements have to be addressed depending on the application. A low-level controller for example must be implemented for realtime use, whereas a task planning component will interact with the robot on a higher abstraction level. Hence, developing robotics software is subject to several constraints such as performance and robustness.

Integrated Grasp and motion planning

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque.We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.