Adaptive Inverse Kinematics Framework for Learning Variable-Length Tool Manipulation in Robotics

Abstract--Conventional robots possess a limited understanding of their kinematics and are confined to preprogrammed tasks, hindering their ability to leverage tools efficiently. Driven by the essential components of tool usage--grasping the desired outcome, selecting the most suitable tool, determining optimal tool orientation, and executing precise manipulations--we introduce a pioneering framework. Our novel approach expands the capabilities of the robot's inverse kinematics solver, empowering it to acquire a sequential repertoire of actions using tools of varying lengths. By integrating a simulation-learned action trajectory with the tool, we showcase the practicality of transferring acquired skills from simulation to real-world scenarios through comprehensive experimentation. Remarkably, our extended inverse kinematics solver demonstrates an impressive error rate of less than 1cm. Furthermore, our trained policy achieves a mean error of 8cm in simulation. Noteworthy, our model achieves virtually indistinguishable performance when employing two distinct tools of different lengths. This research provides an indication of potential advances in the exploration of all four fundamental aspects of tool usage, enabling robots to master the intricate art of tool manipulation across diverse tasks. Tool use is the employment of a device or object held in a robotic gripper or hand to fulfill a task goal. Humans and animals like the New Caledonian crow have learned to use tools to accomplish tasks that they were not previously able to do when using only their own bodies or appendages.