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

TL;DR

This work tackles learning tool use with variable-length tools by extending an inverse kinematics solver to include tool-length awareness and by training a simulation-based policy that generates tool-use trajectories robust to tool length. The approach combines a length-detecting perception module, an offset-based gripper positioning strategy, and a MuJoCo-Gym RL pipeline (evaluated with PPO, TRPO, A2C, and DDPG) to produce transferable actions for the Baxter robot. In simulation, PPO achieved the best end-goal performance ($7.74 cm$ end-position error), and in real hardware the learned policy transferred with near-identical performance for two tool lengths, albeit with some residual noise attributed to grasp slip and contact dynamics. The results demonstrate a viable path toward general tool-learning by fusing extended IK with RL, while highlighting the need for improved perception, physics fidelity, and broader tool diversity for full generalization.

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.

Figures (9)

• Figure 1: Given a tool and task, there are many solutions to accomplishing the task, which is to push the box to the location corresponding to the red box. We aim to create a setup that would facilitate the learning of more general tasks.
• Figure 2: Graphical representation of the complexity of an inverse kinematic solution for the first three joints in the arm. Ju, Zhangfeng, Chenguang Yang, and Hongbin Ma. "Kinematics modeling and experimental verification of Baxter robot." In Control Conference (CCC), 2014 33rd Chinese, pp. 8518-8523. IEEE, 2014.
• Figure 3: Workflow overview: Given a specific task, the inverse kinematics model is extended on the real robot for a determined tool length, and a policy is learned in simulation. The policy and the modified inverse kinematics model are used to determine an action trajectory that is different depending on the tool length, and the action trajectory is passed to the robot for execution.
• Figure 4: Given three images of the tool from different orientations, we detect its length through computer vision techniques.
• Figure 5: Plot of the difference between the target position and the position reached by Baxter's inverse kinematics solver with no tool, a long tool, and a short tool in meters. The accuracy decreases when the solver is extended with a tool but remains under a centimeter.