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

2025-10-30

AI Summary

Conventional robotic systems rely on fixed kinematic models, limiting their ability to generalize across tools of variable lengths. Method: We propose an extended inverse kinematics (IK) framework that explicitly incorporates variable-length tool modeling, unifying tool selection, grasp pose planning, orientation optimization, and precise manipulation. Our approach integrates simulation-driven trajectory generation, reinforcement learning–based policy training, and sim-to-real transfer techniques—marking the first IK formulation to explicitly embed dynamic tool length as a learnable parameter. Contribution/Results: The framework enables seamless multi-tool switching and cross-task skill transfer. Experiments demonstrate sub-centimeter positioning accuracy (<1 cm) in real-world settings and an average 8 cm error in simulation, with consistent performance across two distinct tool lengths. This significantly enhances generalization and robustness in tool manipulation tasks.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Enabling robots to learn variable-length tool manipulation skills
• Expanding inverse kinematics solver for sequential tool-using actions
• Transferring simulation-learned tool manipulation to real-world scenarios

Innovation

• Adaptive inverse kinematics framework for variable-length tools
• Simulation-learned action trajectory transfer to reality
• Extended solver achieves sub-centimeter error in manipulation