arXiv:2605.22986v1 Announce Type: cross Abstract: Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects of behavior. In practice, demonstrations are often imperfect: humans may under-emphasize certain features due to cognitive load or physical difficulty, or the training regime may fail to sufficiently cover all relevant situations. In either case, important features may be underspecified, leading to ambiguity in the learned reward function and misaligned behavior at deployment. We propose a framewor
The increasing deployment of AI systems, particularly in robotics, highlights the critical need for robust and interpretable reward learning to prevent misaligned behaviors and ensure safety.
Achieving reliable and safe AI requires overcoming the challenge of misaligned reward functions, which this research addresses by proposing a framework for robots to actively seek clarifying information from human operators.
This research introduces a method where AI systems, specifically robots, can 'know what to ask' to recover from underspecified reward functions, potentially leading to more trustworthy and adaptable autonomous agents.
- · Robotics Developers
- · AI Safety Researchers
- · Human-Robot Interaction Specialists
- · Industries deploying autonomous systems
- · Companies with brittle or opaque AI deployment strategies
- · Developers relying solely on passive reward learning
Improved reliability and safety in autonomous robotic systems due to better understanding of human intent.
Accelerated adoption of robots in complex environments where human interaction and adaptability are crucial.
New standards and methodologies for human supervision and interaction with intelligent autonomous agents.
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