Incremental learning of humanoid robot behavior from natural interaction and large language models.

ABSTRACT: Natural-language dialog is key for an intuitive human-robot interaction. It can be used not only to express humans' intents but also to communicate instructions for improvement if a robot does not understand a command correctly. It is of great importance to let robots learn from such interaction experiences in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve such incremental learning of complex high-level behavior from natural interaction and demonstrate its implementation on a humanoid robot. Our system deploys large language models (LLMs) for high-level orchestration of the robot's behavior based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both r

SUBMITTER: Barmann L 

PROVIDER: S-EPMC11499633 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature