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

TL;DR

This paper presents a novel inverse kinematics framework enabling robots to manipulate tools of varying lengths, improving their ability to perform complex tasks with high precision and transferring skills from simulation to real-world applications.

Contribution

The paper introduces an extended inverse kinematics solver that learns to handle variable-length tools and demonstrates successful transfer from simulation to real-world scenarios.

Findings

Error rate of less than 1 cm in real-world tests

Mean error of 8 cm in simulation

Performance consistent across different tool lengths

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 1 cm. Furthermore, our trained policy achieves a mean error of 8 cm 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.