arXiv:2605.27095v1 Announce Type: new Abstract: Learning from demonstrations in embodied control is often cast as behavioral cloning, and recent diffusion or flow-matching policies improve this paradigm by modeling multi-modal expert actions. Yet these methods remain offline supervised learners: the policy is trained only on expert states and receives no corrective signal on the states it actually visits. On-policy distillation (OPD) offers a natural remedy, but standard OPD assumes a strong fixed teacher, which is unavailable in demonstration-only control. We propose \textbf{FA-OPD}, an \emph
The paper introduces a method to improve learning from demonstrations in embodied control, leveraging on-policy distillation which is a relevant area of research for more robust AI policy learning.
This research addresses a critical limitation in behavioral cloning by enabling policies to learn from their own actions and receive corrective signals, moving beyond purely offline supervised learning.
The proposed FA-OPD aims to create more adaptive and robust AI policies that can learn and correct themselves in dynamic environments, potentially accelerating the development of more capable autonomous systems.
- · AI developers
- · Robotics companies
- · Embodied AI research
- · Autonomous systems
- · Traditional behavioral cloning methods
- · AI safety concerns (potentially, if not properly controlled)
Improved performance and adaptability of AI agents in real-world scenarios due to better learning from demonstrations.
Faster and more efficient development cycles for robotic and autonomous systems, reducing the need for extensive manual data collection.
Acceleration of the path towards general-purpose AI agents capable of complex tasks in unstructured environments.
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Read at arXiv cs.LG