The paper presents a new generation of torque-controlled lightweight robots (LWR) developed at the Institute of Robotics and Mechatronics of the German Aerospace Center. In order to act in unstructured environments and interact with humans, the robots have design features and control/software functionalities which distinguish them from classical robots, such as: load-to-weight ratio of 1:1, torque sensing in the joints, active vibration damping, sensitive collision detection, as well as compliant control on joint and Cartesian level. Due to the partially unknown properties of the environment, robustness of planning and control with respect to environmental variations is crucial. After briefly describing the main hardware features, the paper focuses on showing how joint torque sensing (as a main feature of the robot) is consequently used for achieving the above mentioned performance, safety, and robustness properties.

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Physical human–robot interaction implies the intersection of human and robot workspaces and intrinsically favors collision. The robustness of the most exposed parts, such as the hands, is crucial for effective and complete task execution of a robot. Considering the scales, we think that the robustness can only be achieved by the use of energy storage mechanisms, e.g. in elastic elements. The use of variable stiffness drives provides a low-pass filtering of impacts and allows stiffness adjustments depending on the task. However, using these drive principles does not guarantee the safety of the human due to the dramatically increased dynamics of such system. The design methodology of an antagonistically tendon-driven hand is explained. The resulting hand, very close to its human archetype in terms of size, weight, and, in particular, grasping performance, robustness, and dynamics, is presented. The hyper-actuated hand is a research platform that will also be used to investigate the importance of mechanical couplings and, in future projects, be the basis of a simplified hand that would still perform daily manipulation tasks.

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Abstract-A robotic ball-catching system built from a multipurpose 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.

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