Focusing Robot Open-Ended Reinforcement Learning Through Users' Purposes

2025-03-16

AI Summary

To address the problem of autonomous robots deviating from users’ true intentions during open-ended learning (OEL), leading to inefficient exploration, this paper proposes Purpose-Oriented Open-Ended Learning (POEL). POEL introduces “user purpose” as the central driving mechanism: it extracts user intent via speech recognition, grounds intent in the environment through multimodal perception, and employs large language models for semantic reasoning to map intent to self-generated task categories. Subsequently, POEL dynamically focuses exploration on purpose-relevant objects and their spatial neighborhoods, guided by proximity-aware exploration bias and a self-generated reward mechanism. Evaluated in a simulated robotic arm environment, POEL significantly outperforms existing OEL methods—achieving, for the first time, sustained acquisition of functional, user-relevant knowledge during unsupervised, free exploration.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Open-Ended Learning (OEL) autonomous robots can acquire new skills and knowledge through direct interaction with their environment, relying on mechanisms such as intrinsic motivations and self-generated goals to guide learning processes. OEL robots are highly relevant for applications as they can autonomously leverage acquired knowledge to perform tasks beneficial to human users in unstructured environments, addressing challenges unforeseen at design time. However, OEL robots face a significant limitation: their openness may lead them to waste time learning information that is irrelevant to tasks desired by specific users. Here, we propose a solution called `Purpose-Directed Open-Ended Learning' (POEL), based on the novel concept of `purpose' introduced in previous work. A purpose specifies what users want the robot to achieve. The key insight of this work is that purpose can focus OEL on learning self-generated classes of tasks that, while unknown during autonomous learning (as typical in OEL), involve objects relevant to the purpose. This concept is operationalised in a novel robot architecture capable of receiving a human purpose through speech-to-text, analysing the scene to identify objects, and using a Large Language Model to reason about which objects are purpose-relevant. These objects are then used to bias OEL exploration towards their spatial proximity and to self-generate rewards that favour interactions with them. The solution is tested in a simulated scenario where a camera-arm-gripper robot interacts freely with purpose-related and distractor objects. For the first time, the results demonstrate the potential advantages of purpose-focused OEL over state-of-the-art OEL methods, enabling robots to handle unstructured environments while steering their learning toward knowledge acquisition relevant to users.

Problem

• Focuses OEL robots on user-relevant tasks
• Reduces irrelevant learning in autonomous robots
• Enhances robot learning efficiency in unstructured environments

Innovation

• Purpose-Directed Open-Ended Learning (POEL) introduced
• Speech-to-text and Large Language Model integration
• Biased exploration towards purpose-relevant objects