Adaptive Robotic Tool-Tip Control Learning (RA-L 2021)

Title: Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State
Authors: Kento Kawaharazuka, Kei Okada, Masayuki Inaba
Accepted at IEEE Robotics and Automation Letters
arxiv - https://arxiv.org/abs/2407.08052

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.