Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State

TL;DR

This paper introduces an adaptive neural network-based method for robotic tool-tip control that accounts for real-time changes in grasping state and deformable tools, validated on two robot types.

Contribution

It presents a novel approach for online adaptation in robotic tool manipulation considering grasping state variations and deformable tools.

Findings

Effective in handling online grasping state changes

Works with deformable tools in real-time

Validated on PR2 and MusashiLarm robots

Abstract

Various robotic tool manipulation methods have been developed so far. However, to our knowledge, none of them have taken into account the fact that the grasping state such as grasping position and tool angle can change at any time during the tool manipulation. In addition, there are few studies that can handle deformable tools. In this study, we develop a method for estimating the position of a tool-tip, controlling the tool-tip, and handling online adaptation to changes in the relationship between the body and the tool, using a neural network including parametric bias. We demonstrate the effectiveness of our method for online change in grasping state and for deformable tools, in experiments using two different types of robots: axis-driven robot PR2 and tendon-driven robot MusashiLarm.