Berthold Bäuml

A Humanoid Two-Arm System for Dexterous Manipulation

This paper presents a humanoid two-arm system developed as a research platform for studying dexterous two-handed manipulation. The system is based on the modular DLR-Lightweight-Robot-III and the DLR-Hand-II. Two arms and hands are combined with a three degrees-of-freedom movable torso and a visual system to form a complete humanoid upper body. In this paper we present the design considerations and give an overview of the different sub-systems. Then, we describe the requirements on the software architecture. Moreover, the applied control methods for two-armed manipulation and the vision algorithms used for scene analysis are discussed

Off-the-shelf vision for a robotic ball catcher

We present a system for catching a flying ball with a robot arm using off-the-shelf components (PC based system) for visual tracking. The ball is observed by a large baseline stereo camera, comparing each image to a slowly adapting reference image. We track and predict the target position using an extended Kalman filter, also taking into account the air drag. The calibration is achieved by simply performing a few throws and observing their trajectories, as well as moving the robot to some predefined positions.

Kinematically optimal catching a flying ball with a hand-arm-system

A robotic ball-catching system built from a multi-purpose 7-DOF lightweight arm (DLR-LWR-III) and a 12 DOF four-fingered hand (DLR-Hand-II) is presented. Other than in previous work a mechatronically complex dexterous hand is used for grasping the ball and the decision of where, when and how to catch the ball, while obeying joint, speed and work cell limits, is formulated as an unified nonlinear optimization problem with nonlinear constraints. Three different objective functions are implemented, leading to significantly different robot movements. The high computational demands of an online realtime optimization are met by parallel computation on distributed computing resources (a cluster with 32 CPU cores). The system achieves a catch rate of > 80% and is regularly shown as a live demo at our institute.

A humanoid upper body system for two-handed manipulation

This video presents a humanoid two-arm system developed as a research platform for studying dexterous two-handed manipulation. The system is based on the modular DLR-Lightweight-Robot-III and the DLR-Hand-II. Two arms and hands are combined with a three degrees-of-freedom movable torso and a visual system to form a complete humanoid upper body. The diversity of the system is demonstrated by showing the mechanical design, several control concepts, the application of rapid prototyping and hardware-in-the-loop (HIL) development as well as two-handed manipulation experiments and the integration of path planning capabilities.

Real-time swept volume and distance computation for self collision detection

We present a real-time self collision detection algorithm applicable for industrial and humanoid robots. The algorithm is based on computing the swept volumes of all bodies and checking them pairwise for collisions. The algorithm operates on joint angle intervals. Such, it does not only test a single or N intermediate configurations but assures safety of a whole movement. Key idea of the new swept volume computation is representing volumes as convex hulls extended by a buffer radius, so called sphere swept convex hulls (SSCH). This leads to tight and compact bounding volumes. The operation set available to model the different joints is strictly conservative and allows for a trade-off between accuracy and computation time. During a configurable timespan the algorithm updates a table of pairwise distances and thus can guarantee hard real-time. It is applied on DLRs humanoid Justin in a sports robotic scenario, where also accuracy and computational performance is evaluated (0.4ms, INTEL T2500@2GHz).

Agile Robot Development (aRD): A Pragmatic Approach to Robotic Software

Mechatronic systems are reaching a new level of complexity, both for the single component and for overall systems making necessary a new software concept for the development and usage of such systems. Here we introduce the agile robot development (aRD) concept, which is a flexible, pragmatic and distributed software design to support and simplify the development of complex mechatronic and robotic systems. It gives easy access to scalable computing performance (even in hard realtime) and is motivated by the abstract view on a robotic system as being a decentral net of calculation blocks and communication links. We discuss design considerations and an implementation of this concept and demonstrate its performance with first applications

Autonomous opening of a door with a mobile manipulator: A case study

Abstract In this paper a robust strategy for opening a door with a mobile manipulator system is described. The considered system is a combination of the DLR-Lightweight-Robot-II and the DLR-Hand-II with a mobile platform. The Cartesian impedance control of the arm serves as a basis for the door opening strategy. In the presentation of the controller the emphasis is put on the treatment of the joint elasticities and on its robustness properties. Then the application of opening a door is described in detail. After grasping and turning the door handle, the arm uses a special impedance behavior to keep the door at a distance while the mobile platform moves through the door hinge. This works without knowledge of the door size and without planning an explicit door opening trajectory.

3D modeling, distance and gradient computation for motion planning: A direct GPGPU approach

The Kinect sensor and KinectFusion algorithm have revolutionized environment modeling. We bring these advances to optimization-based motion planning by computing the obstacle and self-collision avoidance objective functions and their gradients directly from the KinectFusion model on the GPU without ever transferring any model to the CPU. Based on this, we implement a proof-of-concept motion planner which we validate in an experiment with a 19-DOF humanoid robot using real data from a tabletop work space. The summed-up time from taking the first look at the scene until the planned path avoiding an obstacle on the table is executed is only three seconds.

Real-time dense multi-scale workspace modeling on a humanoid robot

Without a precise and up-to-date model of its environment a humanoid robot cannot move safely or act usefully. Ideally, the robot should create a dense 3D environment model in real-time, all the time, and respect obstacle information from it in every move it makes as well as obtain the information it needs for fine manipulation with its fingers from the same map. We propose to use a multi-scale truncated signed distance function (TSDF) map consisting of concentric, nested cubes with exponentially decreasing resolution for this purpose. We show how to extend the KinectFusion real-time SLAM algorithm to the multi-scale case as well as how to compute a multi-scale Euclidean distance transform (EDT) thereby establishing the link to optimization-based planning. We overcome the inability of KinectFusions localization to handle scenes without enough constraining geometry by switching to mapping-with-known-poses based on forward kinematics. The latter is always available and we know when it is precise. The resulting map has the desired properties: It is computed in real-time (7.5 ms per depth frame for a (8 m)3 multi-scale TSDF volume), covers the entire laboratory, does not depend on scene properties (geometry, texture, etc.) and is precise enough to facilitate grasp planning for fine manipulation tasks - all in a single map.

Robust material classification with a tactile skin using deep learning

Attaching a flexible tactile skin to an existing robotic system is relatively easy compared to integrating most other tactile sensor designs. In this paper we show that material classification purely based on the spatio-temporal signal of a flexible tactile skin can be robustly performed in a real world setting. We compare different classification algorithms and feature sets, including features adopted and extended from previous works in tactile material classification and that are based on the signals Fourier spectrum. Our convolutional deep learning network architecture, which we also present here, is directly fed with the raw 24000 dimensional sensor signal and performs best by a large margin, reaching a classification accuracy of up to 97.3%.