Can Modular Finger Control for In-Hand Object Stabilization be accomplished by Independent Tactile Feedback Control Laws?
This addresses the challenge of generalizing grip control across various in-hand manipulation tasks for robotics, though it appears incremental as it builds on existing tactile feedback methods.
The paper tackles the problem of in-hand object stabilization by proposing a modular approach where each finger is controlled by an independent tactile grip controller that predicts and prevents slip using biotac sensor signals, and demonstrates it works for two to five finger grip stabilization without central communication.
Currently grip control during in-hand manipulation is usually modeled as part of a monolithic task, yielding complex controllers based on force control specialized for their situations. Such non-modular and specialized control approaches render the generalization of these controllers to new in-hand manipulation tasks difficult. Clearly, a grip control approach that generalizes well between several tasks would be preferable. We propose a modular approach where each finger is controlled by an independent tactile grip controller. Using signals from the human-inspired biotac sensor, we can predict future slip - and prevent it by appropriate motor actions. This slip-preventing grip controller is first developed and trained during a single-finger stabilization task. Subsequently, we show that several independent slip-preventing grip controllers can be employed together without any form of central communication. The resulting approach works for two, three, four and five finger grip stabilization control. Such a modular grip control approach has the potential to generalize across a large variety of inhand manipulation tasks, including grip change, finger gaiting, between-hands object transfer, and across multiple objects.