arXiv:2509.26627v3 Announce Type: replace-cross Abstract: Designing dense rewards is crucial for reinforcement learning (RL), yet in robotics it often demands extensive manual effort and lacks scalability. One promising solution is to view task progress as a dense reward signal, as it quantifies the degree to which actions advance the system toward task completion over time. We present TimeRewarder, a simple yet effective reward learning method that derives progress estimation signals from passive videos, including robot demonstrations and human videos, by modeling temporal distances between f
The continuous push for more robust and scalable reinforcement learning in robotics necessitates innovative solutions for reward design, moving beyond manual and often brittle approaches.
Learning dense rewards from passive videos can significantly accelerate robot learning and deployment by making it easier to train robots for complex tasks without extensive manual engineering of reward functions.
The development pathway for robotic automation could become faster and more accessible for a wider range of tasks, potentially lowering the barrier to entry for advanced robotic applications.
- · Robotics companies
- · AI researchers
- · Manufacturing sector
- · Logistics sector
- · Human task trainers (for manual reward engineering)
- · Companies relying on traditional, brittle RL reward systems
Robots will be able to learn complex tasks faster and with less human intervention by extracting dense reward signals from existing video data.
This improved learning efficiency could accelerate the development and deployment of autonomous systems across various industries, from manufacturing to service.
A breakthrough in reward learning could be a critical step towards more general-purpose AI agents and advanced humanoid robotics, collapsing broader workflows.
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Read at arXiv cs.LG