Learning for autonomy in the wild : theory, algorithms, and practice



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How can autonomous systems learn to operate in the wild, i.e., complex, dynamic, and uncertain real-world environments? Despite recent and significant breakthroughs in artificial intelligence, there is still a tremendous gap between its current capabilities and what we need to do to develop systems that can autonomously operate in the wild. We aim to bridge this gap by addressing a few key challenges of learning in the wild. These challenges include learning with extremely scarce amounts of data, learning safely from a single and ongoing trial, learning to generalize to unseen situations, and learning with uncertainty-aware and explainability considerations for trustworthy human-robot interactions. We take an opinionated approach to address these challenges and argue that data are never the only source of knowledge available during training, and modern learning techniques should not treat them as such. Instead, we demonstrate that merging modern learning techniques' efficiency at extracting patterns from data with existing knowledge on how the world works is key for autonomous systems to achieve learning in the wild. This existing knowledge on how the world works may stem from structural knowledge such as fundamental principles of physics, qualitative expert knowledge such as design or mechanical constraints, or contextual knowledge such as formal specifications on the underlying task. Thus, by leveraging prior knowledge into learning through formal techniques, we propose data-driven modeling and control approaches that enable autonomous systems to operate even under severely limited amounts of data, such as streaming data from a single and ongoing trial. We additionally demonstrate that the data-driven approaches generalize beyond the training regime, improve explainability over traditional black-box models, and exhibit principled uncertainty awareness. Specifically, we focus on theoretical analyses that quantify the benefits of exploiting prior knowledge as inductive bias in terms of data efficiency, safety, computational requirements, and optimality of learning. We derive these theoretical analyses through novel ideas at the intersection of control, learning, and formal methods. Based on the theoretical insights, we develop practical and computationally efficient algorithms, some of which have provable performance, real-time, and safety guarantees. To validate the effectiveness of our algorithms, we conduct experiments in high-fidelity robotics and flight simulators, as well as on real-world hardware such as a Toyota Supra car and a custom-built hexacopter. Remarkably, when applied in real-world settings, our algorithms provide high performance for control tasks that push the system beyond the limits of the prior knowledge and data coverage, despite being trained on only a handful of system trajectories or a few minutes worth of data.


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