A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder (1711.00614v1)

Abstract: The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem. We introduce a long short-term memory based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution. We also introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score and a state-based threshold. For evaluations with 1,555 robot-assisted feeding executions including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve (AUC) of 0.8710 than 5 other baseline detectors from the literature. We also show the multimodal fusion through the LSTM-VAE is effective by comparing our detector with 17 raw sensory signals versus 4 hand-engineered features.

• The paper introduces an LSTM-VAE model that fuses visual, haptic, kinematic, and auditory signals without heavy feature engineering.
• It reconstructs multimodal inputs and employs state-based thresholding to dynamically improve anomaly detection during different task phases.
• Empirical validation on 1,555 feeding executions shows a competitive AUC of 0.8710, outperforming five baseline detectors.

Multimodal Anomaly Detection in Robot-Assisted Feeding

The paper presents a method for detecting anomalies in robot-assisted feeding using a Long Short-Term Memory-based Variational Autoencoder (LSTM-VAE). The primary aim is to improve the safety and effectiveness of assistive robotic systems, especially in tasks like feeding, where detecting anomalies can mitigate potential hazards.

Key Contributions

1. Multimodal Signal Fusion: The paper addresses the complex issue of fusing high-dimensional and heterogeneous sensory signals for anomaly detection. The proposed LSTM-VAE approach effectively integrates multiple sensory inputs, including visual, haptic, kinematic, and auditory data, without substantial feature engineering.
2. Reconstruction-based Detection: The model reconstructs multimodal inputs and uses the reconstruction log-likelihood to detect anomalies. A successful reconstruction is indicative of a non-anomalous process, while significant deviations suggest anomalies.
3. State-based Thresholding: An innovative aspect of the research is the introduction of a state-based thresholding mechanism, which adapts during different phases of task execution based on the state of the task, thereby improving sensitivity and reducing false alarms.
4. Empirical Validation: The approach is evaluated using data from 1,555 robot-assisted feeding executions, involving both anomalous and non-anomalous tasks. The LSTM-VAE demonstrated superior performance with an AUC of 0.8710, outperforming five baseline detectors.

Numerical and Comparative Results

• The LSTM-VAE achieved an AUC 0.044 higher than the HMM-GP approach used previously by the authors.
• When utilizing 17-dimensional raw sensory signals, the LSTM-VAE exhibited a 0.064 higher AUC compared to using 4-dimensional hand-engineered features, highlighting the efficiency of direct multimodal signal processing.
• A notable advancement was seen in integrating state-based thresholding, which provided a more adaptive decision-making boundary compared to fixed thresholds.

Practical and Theoretical Implications

Practically, implementing the LSTM-VAE model in assistive robots can significantly enhance their reliability, thereby increasing user confidence and adoption. The ability to detect a wide range of anomalies using multimodal inputs equips robots with a more nuanced understanding of their operating environment.

Theoretically, this research contributes to the fields of anomaly detection, multimodal signal processing, and robotics. By leveraging the capabilities of LSTM and VAE, the research merges time-series analysis with probabilistic generative modeling, offering a robust framework for understanding and predicting complex robotic behaviors.

Future Directions

Future research could further refine the approach by exploring more sophisticated LSTM configurations or alternative architectures, such as Transformer-based models, which might improve the model's ability to handle even more complex and variable tasks. Additionally, extending the model's capabilities to other assistive tasks beyond feeding could provide a broader validation of its applicability and robustness.

In summary, the paper demonstrates an effective integration of LSTM-VAE for multimodal anomaly detection in robot-assisted feeding, achieving significant improvements over existing methods and highlighting pathways for further research in this domain.