From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation
arXiv:2603.15600v2 Announce Type: replace-cross Abstract: Accurate process supervision remains a critical challenge for long-horizon robotic manipulation. A primary bottleneck is that current video MLLMs, trained primarily under a Supervised Fine-Tuning (SFT) paradigm, function as passive "Observers" that recognize ongoing events rather than evaluating the current state relative to the final task goal. In this paper, we introduce PRIMO R1 (Process Reasoning Induced Monitoring), a 7B framework that transforms video MLLMs into active "Critics". We leverage outcome-based Reinforcement Learning to
The rapid advancements in large language models and reinforcement learning are converging to enable more sophisticated robotic control and understanding.
This development addresses a critical bottleneck in robotic manipulation by moving from passive observation to active, goal-oriented process reasoning, significantly enhancing autonomy and capability.
Robots equipped with these new frameworks will be able to evaluate tasks against final goals and correct processes proactively, rather than merely recognizing ongoing events.
- · Robotics companies
- · AI software developers
- · Automation industries
- · Manufacturing
- · Tasks requiring constant human oversight for complex manipulation
Robotic systems achieve greater autonomy and reliability in performing complex, long-horizon tasks.
Accelerated adoption of advanced robotics in sectors like logistics, healthcare, and precision manufacturing due to enhanced operational capabilities.
The development of more generalized robotic intelligence capable of adapting to novel tasks with minimal human intervention, broadening the scope of robotic application significantly.
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Read at arXiv cs.AI