ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input

TL;DR

This paper introduces ILeSiA, a vision-based system that enables robots to recognize risky situations in real-time using a Gaussian Process model, allowing for rapid, safe deployment and fault detection from minimal training data.

Contribution

The paper presents a novel Gaussian Process-based approach for robot situational awareness using camera input, capable of detecting known and unknown faults with minimal data.

Findings

Reliable detection of known and novel faults with a single example.

Outperforms standard MLP in fault detection.

Enables proactive safety measures in robotic manipulation.

Abstract

Learning from demonstration is a promising approach for teaching robots new skills. However, a central challenge in the execution of acquired skills is the ability to recognize faults and prevent failures. This is essential because demonstrations typically cover only a limited set of scenarios and often only the successful ones. During task execution, unforeseen situations may arise, such as changes in the robot's environment or interaction with human operators. To recognize such situations, this paper focuses on teaching the robot situational awareness by using a camera input and labeling frames as safe or risky. We train a Gaussian Process (GP) regression model fed by a low-dimensional latent space representation of the input images. The model outputs a continuous risk score ranging from zero to one, quantifying the degree of risk at each timestep. This allows for pausing task execution in unsafe situations and directly adding new training data, labeled by the human user. Our experiments on a robotic manipulator show that the proposed method can reliably detect both known and novel faults using only a single example for each new fault. In contrast, a standard multi-layer perceptron (MLP) performs well only on faults it has encountered during training. Our method enables the next generation of cobots to be rapidly deployed with easy-to-set-up, vision-based risk assessment, proactively safeguarding humans and detecting misaligned parts or missing objects before failures occur. We provide all the code and data required to reproduce our experiments at imitrob.ciirc.cvut.cz/publications/ilesia.