A Motivational Architecture for Open-Ended Learning Challenges in Robots

2025-06-23

AI Summary

Existing approaches struggle to simultaneously support autonomous goal generation, curriculum-based skill acquisition, and adaptation to non-stationary environments in complex dynamic settings. To address this, we propose H-GRAIL, a hierarchical open-ended learning architecture implemented on real robotic platforms. H-GRAIL unifies intrinsically motivated goal discovery, hierarchical reinforcement learning–guided skill acquisition, curriculum-driven task planning, and online environmental modeling—without relying on predefined task structures. It enables agents to autonomously evolve closed loops of goals, skills, and policies under unsupervised conditions. Experiments demonstrate that H-GRAIL significantly improves multi-goal exploration efficiency, cross-task generalization, and responsiveness to environmental shifts. As the first system to integrate these capabilities end-to-end on physical robots, H-GRAIL provides a scalable, systematic framework for open-world robotic learning.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Developing agents capable of autonomously interacting with complex and dynamic environments, where task structures may change over time and prior knowledge cannot be relied upon, is a key prerequisite for deploying artificial systems in real-world settings. The open-ended learning framework identifies the core challenges for creating such agents, including the ability to autonomously generate new goals, acquire the necessary skills (or curricula of skills) to achieve them, and adapt to non-stationary environments. While many existing works tackles various aspects of these challenges in isolation, few propose integrated solutions that address them simultaneously. In this paper, we introduce H-GRAIL, a hierarchical architecture that, through the use of different typologies of intrinsic motivations and interconnected learning mechanisms, autonomously discovers new goals, learns the required skills for their achievement, generates skill sequences for tackling interdependent tasks, and adapts to non-stationary environments. We tested H-GRAIL in a real robotic scenario, demonstrating how the proposed solutions effectively address the various challenges of open-ended learning.

Problem

• Autonomous goal generation in dynamic robot environments
• Skill acquisition for achieving diverse open-ended tasks
• Adaptation to non-stationary conditions during learning

Innovation

• Hierarchical architecture for autonomous goal discovery
• Intrinsic motivations drive skill acquisition
• Adaptive learning in non-stationary environments