Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning
arXiv:2605.28372v1 Announce Type: new Abstract: Imitation learning (IL) from a state-based reinforcement learning (RL) policy is a common approach to overcome the curse of dimensionality in complex and high-dimensional observation spaces prevalent in robotics. This paper addresses the irreducible imitation gap that emerges when teacher and student are learned in isolation, and the teacher policy has the liberty to rely on privileged state information that the student cannot infer from its observations. Instead of improving poor student performance with RL finetuning after IL, which often requi
The paper directly addresses a fundamental challenge in complex robotic learning, crucial for advancing AI agent capabilities in real-world scenarios.
Improving imitation learning by aligning teacher and student representations reduces the imitation gap, making practical deployment of advanced RL policies more feasible.
This approach offers a more direct and efficient method to bridge the performance gap between privileged teacher policies and observation-limited student policies, potentially accelerating autonomous system development.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Companies reliant on human-driven delicate tasks
- · Less efficient imitation learning methodologies
More robust and generalizable AI policies for autonomous robots can be developed more quickly.
Accelerated development leads to faster commercialization of humanoid robots and other advanced robotic systems.
Increased adoption of robots and autonomous agents in various sectors, impacting labor markets and industrial productivity.
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