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Imitation learning is a powerful data-driven paradigm that enables machines to acquire advanced skills at a human-level proficiency by learning from demonstrations provided by humans or other agents. This approach has found applications in various domains such as robotics, autonomous driving, and text generation. However, the effectiveness of imitation learning depends heavily on the quality of the demonstrations it receives. Human demonstrations can often be inadequate, partial, environment-specific, and sub-optimal. For example, experts may only demonstrate successful task completion in ideal conditions, neglecting potential failure scenarios and important aspects of system safety considerations. The lack of diversity in the demonstrations can introduce bias in the learning process and compromise the safety and robustness of the learning systems. Additionally, current imitation learning algorithms primarily focus on replicating expert behaviors and are thus limited to learning from successful demonstrations alone. This inherent inability to learn to avoid failure is a significant limitation of existing methodologies. As a result, when faced with real-world uncertainties, imitation learning systems encounter challenges in ensuring safety, particularly in critical domains such as autonomous vehicles, healthcare, and finance, where system failures can have serious consequences. Therefore, it is crucial to develop mechanisms that ensure safety, reliability, and transparency in the decision-making process within imitation learning systems. To address these challenges, this thesis proposes innovative approaches that go beyond traditional imitation learning methodologies by enabling imitation learning systems to incorporate explicit task specifications provided by human designers. Inspired by the idea that humans acquire skills not only by learning from demonstrations but also by following explicit rules, our approach aims to complement expert demonstrations with rule-based specifications. We show that in machine learning tasks, experts can use specifications to convey information that can be difficult to express through demonstrations alone. For instance, in safety-critical scenarios where demonstrations are infeasible, explicitly specifying safety requirements for the learner can be highly effective. We also show that experts can introduce well-structured biases into the learning model, ensuring that the learning process adheres to correct-by-construction principles from its inception. Our approach, called ‘specification-guided imitation learning’, seamlessly integrates formal specifications into the data-driven learning process, laying the theoretical foundations for this framework and developing algorithms to incorporate formal specifications at various stages of imitation learning. We explore the use of different types of specifications in various types of imitation learning tasks and envision that this framework will significantly advance the applicability of imitation learning and create new connections between formal methods and machine learning. Additionally, we anticipate significant impacts across a range of domains, including robotics, autonomous driving, and gaming, by enhancing core machine learning components in future autonomous systems and improving their performance, safety, and reliability. |
