arXiv:2605.20648v1 Announce Type: cross Abstract: Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skills without retraining. Modern generative policies model distributions over action trajectories alone, thus are unable to reason about the symbolic outcomes required for robust composition. We propose that skills should jointly model action trajectories and the symbolic outcomes they induce. To address this gap, we introduce Predicate Action Skills (PACTS), a class of
The paper addresses a critical generalization gap in Learning from Demonstration (LfD) for robotics, emerging as AI models advance beyond mere trajectory generation to more symbolic reasoning.
This research is crucial for advancing AI agent capabilities in robotics, allowing for more robust, generalizable, and zero-shot skill composition, which is a key bottleneck for real-world deployment.
Robots will be able to learn complex skills more efficiently and apply them to novel composite tasks without extensive retraining, accelerating the development of more capable autonomous systems.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Robot developers
- · Companies reliant on highly specialized, single-task robots
- · Traditional LfD approaches without symbolic reasoning
More versatile robots capable of performing a wider array of tasks in unstructured environments.
Reduced development costs and faster deployment of advanced robotic systems across various industries.
Accelerated adoption of humanoid robots and other autonomous agents in complex, dynamic settings, impacting labor markets and industrial processes.
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Read at arXiv cs.AI