arXiv:2606.00950v1 Announce Type: new Abstract: Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors due to uniform exploration. Guided skill discovery (GSD) addresses this issue by incorporating human intent to focus exploration on meaningful regions. However, existing GSD methods typically require training additional guidance models, and rely on pre-defined rules or expert demonstration, which can be ineffective under sparse, online-collected human feedback. To overcome this, we propose COLLIE
The proliferation of complex AI systems necessitates more efficient and safer methods for skill acquisition, moving beyond rudimentary exploration to guided, semantically coherent learning.
Improving unsupervised skill discovery with human intent and online feedback without requiring extensive training or expert demonstrations accelerates AI development and deployment in real-world scenarios.
This research outlines a methodology for AI agents to learn diverse, useful behaviors more efficiently by integrating granular human intent, potentially leading to more robust and adaptable AI systems.
- · AI developers
- · Robotics industry
- · Automation companies
- · Human-AI interaction researchers
- · Developers reliant on exhaustive manual labeling
- · Systems requiring extensive pre-training
- · Companies with less sophisticated skill discovery methods
AI agents begin to learn new complex skills with less supervision and in a more targeted manner.
The development cycle for advanced AI applications shortens, leading to faster deployment of autonomous systems.
AI systems become more capable across a wider range of unstructured environments, accelerating adoption in various industries.
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Read at arXiv cs.LG