Robot Movement Anomaly Detection in Latent Embedding Space
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Solution Overview
Problem
Robots face challenges in consistently performing tasks due to difficulty in training and potential for anomalous behavior, which can lead to errors causing damage to themselves or objects, and existing systems struggle to efficiently detect and prevent such anomalies.
Innovation Solution
A computing system employs an anomaly detection model to analyze latent space embeddings generated from sensor data, using encoder and reinforcement learning models to identify anomalous states and prevent further actions when anomalies are detected, thereby improving task efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is used to train robots to complete tasks through trial-and-error, then the robot's ability to learn and adapt to different tasks is improved, but the training time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining anomaly specifications and embedding space regions before actual robot operation. The anomaly detection model is pre-trained with specifications that define what constitutes anomalous behavior, allowing the robot to immediately utilize these pre-established criteria without requiring extensive trial-and-error training during deployment.
2Productivity
If the robot performs actions through trial-and-error learning, then the robot can maximize rewards and perform tasks correctly, but the robot may make errors that cause damage to itself or objects during the learning process
Solution Approach 1:
The system applies preliminary anti-action by establishing anomaly detection specifications before the robot begins task execution. These specifications define regions in embedding space that represent harmful or erroneous behaviors. The real-time anomaly detection continuously monitors robot states and prevents actions that would lead to damage, counteracting potential harmful effects before they occur.
Solution Approach 2:
The system implements feedback by continuously monitoring the robot's state through sensor data, encoding it into embedding space, and comparing it against predefined anomaly specifications. When the robot's state approaches or enters an anomalous region, the system provides feedback to prevent the erroneous action, allowing the robot to learn task completion while avoiding harmful behaviors through real-time correction.
3Reliability
If existing systems attempt to detect anomalies during robot operation, then safety can be improved, but the detection efficiency and speed are insufficient to prevent damage in real-time
Solution Approach 1:
The system replaces traditional mechanical or rule-based anomaly detection with a machine learning-based embedding space approach. Sensor data is encoded into compact embedding vectors that capture essential robot states, and anomaly specifications define regions in this mathematical space. This substitution enables highly efficient real-time detection by comparing current embeddings against predefined anomalous regions, significantly improving both reliability and detection speed.
Data Source
AI summary
Provided is a process, including: obtaining, with a computer system, access to a specification indicating which regions of an embedding space are designated as anomalous relative to vectors in the embedding space characterizing past behavior of a first instance of a dynamical system; receiving, with the computer system, multi-channel input indicative of a state of a second instance of the dynamical system; and classifying, with the computer system, whether the state of the second instance of the dynamical system is anomalous by: encoding the multi-channel input into a vector in the embedding space; causing the specification to be applied to the vector; obtaining a result of applying the specification to the vector; and classifying whether the state of the second instance of the dynamical system is anomalous based on the result; and storing the classification in memory.