What can be learned from human reach-to-grasp movements for the design of robotic hand-eye systems?

Abstract

I n the field of robot motion control, visual servoing has been proposed as the suitable strategy to cope with imprecise models and calibration errors. Remaining problems such as the necessity of a high rate of visual feedback are deemed to be solvable b y the development of real-time vision modules. However, human grasping, which still outshines its robotic counterparts especially with respect to robustness and flexibility, definitely requires only sparse, asynchronous visual feedback. W e therefore examined current neuroscientific models for the control of human reach-to-grasp movements with the emphasis lying on the visual control strategy used. From this, we developed a control model that unifies the two robotic strategies look-then-move and visual servoing, thereby compensating the problems that each strategy shows when used alone.

Teleoperation for a ball-catching task with significant dynamics

“…More complex trajectories seem to be well described by superpositions of several simple ones, as proposed in Morasso and Mussa Ivaldi (1982) and further investigated in Milner (1992). Similar bell-shaped velocity profiles can be synthesized using different optimization criteria, such as minimizing jerk (the time derivative of acceleration), joint torque change, energy, or force (Hauck et al, 1999;Kawato, 1996).…”

“…In these cases, there does not seem to be enough time for a conscious continuous feedback loop to correct the hand position, but rather an initial estimate is made of the ball trajectory, and the hand moved roughly toward a point of intercept. If there is still time to react, this position can be corrected one or more times to produce an accurate catch (Hauck, Sorg, & Schenk, 1999). Evidence towards this is that one can distinguish a distinct trajectory towards an initial catch position estimate, and additional shorter distinct trajectories that correct this.…”

Minimum jerk based prediction of user actions for a ball catching task

“…Observations in [3] and [12] on the motions that humans make when freely catching a thrown ball indicate that they start by moving towards the expected point of impact with a distinct MJ-type reaching motion, and later add smaller corrective MJ-type motions to accurately catch the ball.…”

“…If a person were to catch the ball directly then s/he would perform a ballistic arm movement [2] [3] followed by some correction. The strategy could be thought of as "throw the arm to be close to the correct position and use the added time to obtain an improved estimate, which is then executed as a small correction".…”

Visual Guided Approach-to-Grasp for Humanoid Robots

“…Vision is a very important non-contact measurement method for robots. Especially in the field of humanoid robots, where the robot works in an unstructured and complex environment designed for human, visual control can make the robot more robust and flexible to unknown changes in the environment (Hauck et al, 1999). Humanoid robot equipped with vision system is a typical hand-eye coordination system.…”