arXiv:2606.31377v1 Announce Type: cross Abstract: Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to changes in environments and object configurations. This work proposes Stage-Transition Dense Reward (STDR), a visual reward-learning framework that converts unstructured expert videos into logically grounded dense rewards for training RL agents from scratch. STDR leverages semantic understanding to infer a task's stage structure from demonstrations, and delivers two
The development of more sophisticated AI models and increasing research into robotic manipulation necessitates improved reward mechanisms to overcome the limitations of sparse reinforcement learning. This work aligns with the ongoing push for more autonomous and general-purpose robotic systems.
This research provides a method to improve the efficiency and applicability of reinforcement learning in robotics by automating the creation of dense reward signals, which is a critical bottleneck for complex real-world tasks. It accelerates the development of advanced robotic capabilities.
The ability to automatically generate dense rewards from expert demonstrations, particularly in visually rich environments, reduces the manual effort and brittleness associated with current reward engineering approaches. This simplifies the training of robotic agents for long-horizon tasks.
- · Robotics companies
- · AI research labs
- · Automation industry
- · Manufacturers adopting advanced robotics
- · Companies reliant on highly-specialized, manually-programmed robotic systems
Increased pace of development and deployment of reinforcement learning-based robotic systems in diverse applications.
Reduced cost and complexity of developing and training robots, leading to wider adoption across sectors.
Accelerated path towards general-purpose, autonomous humanoid robots capable of complex multi-stage tasks in unstructured environments.
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Read at arXiv cs.AI