Core Concepts
Uncertainty is crucial for robots to efficiently learn and act robustly in the physical world. Quantifying and leveraging uncertainty can enhance the generalizability and decision-making capabilities of embodied AI agents.
Abstract
The article discusses the importance of uncertainty in robotics and provides an overview of the various sources of uncertainty that embodied AI agents may encounter. It highlights the need to balance the trade-off between accuracy, uncertainty, and computational complexity when dealing with uncertainty.
The content covers two main paradigms where uncertainty and diversity of predictions prove beneficial: the evaluative paradigm and the generative paradigm. In the evaluative paradigm, the robot learns a model that captures its uncertainty about the world, which can then be used to generate diverse hypotheses and make risk-averse or risk-seeking decisions. In the generative paradigm, the goal is to generate diverse worlds, scenarios, or data, which can be used to learn robust machine learning models or identify failure modes and out-of-distribution scenarios.
The article then delves into the different types of uncertainty, namely aleatoric (known unknowns) and epistemic (unknown unknowns) uncertainty, and discusses various techniques to quantify and measure uncertainty, such as variance, entropy, negative log probability, Mahalanobis distance, and f-divergences. It also covers popular probabilistic and non-probabilistic methods for uncertainty quantification, including ensembles, Monte Carlo dropout, Laplace approximation, variational inference, Markov Chain Monte Carlo, and conformal prediction.
Furthermore, the article discusses the importance of calibrating the estimated uncertainties and provides an overview of techniques like Expected Calibration Error (ECE) and Adaptive Calibration Error (ACE) to assess the correctness of the uncertainty estimates.
Finally, the content explores how uncertainty can be leveraged in various robotics tasks, such as mapping the environment, localization and tracking, object detection and pose estimation, semantic segmentation, imagination and future prediction, agent modeling and human-AI alignment, and out-of-distribution detection. It also discusses how uncertainty can be propagated from perception to decision-making and how it can be used for exploration and learning robust policies.
Stats
Robots need to efficiently learn how to robustly act in the unrestrained physical world.
Uncertainty can arise from various sources, including physical limitations, model limitations, partial observability, environment dynamics, and domain shifts.
Uncertainty can be categorized into aleatoric (known unknowns) and epistemic (unknown unknowns) uncertainty.
Uncertainty can be measured using metrics like variance, entropy, negative log probability, Mahalanobis distance, and f-divergences.
Techniques for quantifying uncertainty include ensembles, Monte Carlo dropout, Laplace approximation, variational inference, Markov Chain Monte Carlo, and conformal prediction.
Uncertainty calibration can be assessed using metrics like Expected Calibration Error (ECE) and Adaptive Calibration Error (ACE).
Quotes
"Uncertainty has long been a critical area of study in robotics, particularly when robots are equipped with analytical models. As we move towards the widespread use of deep neural networks in robots, which have demonstrated remarkable performance in research settings, understanding the nuances of uncertainty becomes crucial for their real-world deployment."
"Characterizing the likelihood of events and actions about robots and the physical world they operate in inherently involves uncertainty, whether represented as probabilities, sets, or any other form. However, some techniques for handling uncertainty tend to be computationally expensive and sometimes even inaccurate, thereby defeating the purpose of working with uncertainty for efficient learning and enhancing robustness."