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About this paper
Abhiroop Ajith, Gokul Narayanan, Kyle Coelho, Tingji Zhao, Yash Shahapurkar, Brian Zhu, Melih Erdogan, Ted Krubasik and 2 more
RoboMonitor learns runtime signals for robot task execution from unlabeled trajectories before fine-tuning on a small set of monitoring annotations. Its predictive representation pre-training and temporal consistency objectives improve phase recognition and prediction stability, reaching 93.09% phase accuracy with 52 labeled episodes and supporting closed-loop recovery in simulated and real manipulation trials.
Key insights
- Predicting future visual representations rather than pixels encourages features that encode action-relevant temporal changes while allowing robot state and commands to be excluded at deployment.
- Predictive pre-training is most beneficial for phase recognition under limited supervision: 52 labeled episodes outperform the phase accuracy of several baselines trained with 100 episodes.
- Monitoring quality must include temporal stability and closed-loop behavior, not only classification metrics, because predictions directly influence task progression and recovery decisions.
Summary by alphaXiv.
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