Impedance Behaviors for Two-handed Manipulation: Design and Experiments

Abstract

The control of humanoid manipulators is very challenging due to the large number of degrees of freedom and the resulting redundancy. Using joint-level control complex planning algorithms are needed to accomplish tasks. For intuitive operation and hence short development times of applications high-level control interfaces are needed. Furthermore, for many tasks it is desirable to define an impedance behavior in task space. In this paper a flexible control law is proposed which offers object-level impedances for two-handed manipulation. The controller structure is based on the wellknown compliance control law. The main contributions of this work are the way how to combine several potential functions for two-handed manipulation and the experimental validation of hand-arm coordination. The controller is implemented on DLR's humanoid manipulator Justin and its performance is demonstrated experimentally by unscrewing a can and motion of a grasped box.

Whole-Body Impedance Control of Wheeled Humanoid Robots

“…Numerous solutions to specific control problems exist such as in the fields of collision avoidance [SGJG10, DSASO + 07], manipulator singularity avoidance [Ott08], singularity avoidance for wheeled platforms [CPHV09], dual-arm manipulation [WOH07], or joint limit avoidance [MCR96]. Particularly since the early 2000s, more and more whole-body controllers that implement several of these objectives simultaneously have been released thanks to the availability of suitable simulation models and hardware.…”

Grasp synthesis in cluttered environments for dexterous hands

“…Autonomous manipulation has been a major goal in robotics for many years and has spawned numerous platforms such as the ARMAR [1], Dexter [2], Domo [3], HRP2 [4], Justin [5], the NASA Robonaut [6], STAIR [7], and UMan [8]. There is renewed interest in moving away from tele-operation, manuallyscripted grasps, and simple scenes, to grasping with dexterous hands in unstructured human environments.…”

When hard realtime matters: Software for complex mechatronic systems

“…Typical examples for computationally demanding controllers are gravity compensation, impedance control [19], where e.g. touching the finger tip can sensitively move the whole upper body, or object impedance, where a virtual imepdance can be assigned to an object, which is grasped with both hands by cooperatively controlling all finger, arm and torso joints [20].…”