InstructRobot: A Model-Free Framework for Mapping Natural Language Instructions into Robot Motion

Iury Cleveston, Alana Santana Correia, Paula Dornhofer Paro Costa, Ricardo R. Gudwin, Alexandre da Silva Simões, Esther Luna Colombini

Keywords: Reinforcement Learning, Human-Robot Interaction, Natural Language Instructions, Robot Manipulation, Model-Free Learning, Inverse Kinematics, Language Grounding

TL;DR: Our framework teaches a robot to follow natural language instructions by learning both language and its own physical movements from scratch, eliminating the need for large datasets or prior kinematic models.

Abstract: The ability to communicate with robots using natural language is a significant step forward in human-robot interaction. However, accurately translating verbal commands into physical actions is promising, but still presents challenges. Current approaches require large datasets to train the models and are limited to robots with a maximum of 6 degrees of freedom. To address these issues, we propose a framework called InstructRobot that maps natural language instructions into robot motion without requiring the construction of large datasets or prior knowledge of the robot's kinematics model. InstructRobot employs a reinforcement learning algorithm that enables joint learning of language representations and inverse kinematics model, simplifying the entire learning process. The proposed framework is validated using a complex robot with 26 revolute joints in object manipulation tasks, demonstrating its robustness and adaptability in realistic environments. The framework can be applied to any task or domain where datasets are scarce and difficult to create, making it an intuitive and accessible solution to the challenges of training robots using linguistic communication.

Submission Number: 55