Foundational World Models Accurately Detect Bimanual Manipulator Failures

TL;DR

This paper introduces a probabilistic world model using a pretrained vision foundation model to detect failures in bimanual manipulators, significantly improving anomaly detection accuracy with fewer parameters.

Contribution

It develops a novel, uncertainty-based failure detection method leveraging a compressed latent space and conformal prediction, and introduces a new dataset for bimanual manipulation failure analysis.

Findings

Outperforms baseline anomaly detection methods in failure detection rate.

Uses approximately 1/20th of the parameters of comparable models.

Achieves 3.8% higher failure detection accuracy than the next-best approach.

Abstract

Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators are no exception; these robots have vast state spaces comprised of high-dimensional images and proprioceptive signals. Explicitly defining failure modes within such state spaces is infeasible. In this work, we overcome these challenges by training a probabilistic, history informed, world model within the compressed latent space of a pretrained vision foundation model (NVIDIA's Cosmos Tokenizer). The model outputs uncertainty estimates alongside its predictions that serve as non-conformity scores within a conformal prediction framework. We use these scores to develop a runtime monitor, correlating periods of high uncertainty with anomalous failures. To test these methods, we use the simulated Push-T environment and the Bimanual Cable Manipulation dataset, the latter of which we introduce in this work. This new dataset features trajectories with multiple synchronized camera views, proprioceptive signals, and annotated failures from a challenging data center maintenance task. We benchmark our methods against baselines from the anomaly detection and out-of-distribution detection literature, and show that our approach considerably outperforms statistical techniques. Furthermore, we show that our approach requires approximately one twentieth of the trainable parameters as the next-best learning-based approach, yet outperforms it by 3.8% in terms of failure detection rate, paving the way toward safely deploying manipulator robots in real-world environments where reliability is non-negotiable.