arXiv:2606.19752v1 Announce Type: cross Abstract: Long-horizon robot manipulation policies trained with reward shaping can still exploit dense rewards through inefficient interaction, while rare efficient behaviors may be forgotten during training. We argue that temporal efficiency itself provides a powerful and underutilized source of self-supervision for reinforcement learning. We introduce Temporal Self-Imitation Learning (TSIL), a reinforcement learning framework that mines temporally efficient successful trajectories generated during learning and converts them into reusable supervision fo
The continuous drive for more efficient and robust reinforcement learning algorithms for robotics necessitates novel approaches to leverage internal learning dynamics.
This development offers a method to significantly improve the efficiency and reliability of robot manipulation, moving closer to deployable autonomous systems in complex environments.
Robot learning can now leverage its own temporally efficient actions as a direct source of supervision, potentially leading to faster skill acquisition and more robust policies.
- · Robotics companies
- · AI researchers
- · Automation sector
- · Logistics and manufacturing
- · Manual labor in highly repetitive tasks
- · Traditional, less data-efficient RL methods
More capable and efficient robot manipulation policies are developed and deployed faster.
Increased adoption of robotic systems in sectors requiring fine motor skills and complex interaction.
Accelerated development of general-purpose humanoid robots capable of emulating human-like dexterity and learning.
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Read at arXiv cs.AI