How can we enable agents to efficiently and safely learn online, from interaction with the real world? I will first present safe Bayesian optimization, where we quantify uncertainty in the unknown objective and constraints, and, under some regularity conditions, can guarantee both safety and convergence to a natural notion of reachable optimum. I will then consider Bayesian model-based deep reinforcement learning, where we use the epistemic uncertainty in the world model to guide exploration while ensuring safety. Lastly I will discuss how we can meta-learn flexible probabilistic models from related tasks and simulations, and demonstrate our approaches on real-world applications, such as robotics tasks and tuning the SwissFEL Free Electron Laser.

Autonomous robots are becoming increasingly prevalent in our daily existence, from navigating our roads and sky, assisting in households and warehouses, conducting daring search and rescue missions, and even exploring the frontiers of space. Yet building robots that can safely and fluently interact with humans in a trusted manner remains an elusive task. Ensuring robots can keep a sufficiently safe distance from humans is at odds with fluent interactions, yet humans are remarkable at seamlessly avoiding collision in crowded settings. In this talk, we will study how humans engage in safe and fluent multi-agent interactions and how this can be applied to robot decision-making and control. In the first half of the talk, I will introduce the notion of safety concepts and demonstrate how we can tractably synthesize data-driven safety concepts using control theoretic tools as inductive biases. These data-driven safety concepts are designed to capture how humans think about safety in real-world scenarios more accurately. In the second half, I will present recent work investigating how fluent motion can lead to safer interactions. Specifically, I will show that legible and proactive robot behavior can lead to prosocial interactions. This talk aims to revisit how safety is defined and rethink how safety and fluency can be more compatible with one another in human-robot interactions.

2024 is off to an exciting start with enormous enthusiasm for humanoids and other robots based on recent advances in "end-to-end" large robot action models. Initial results are promising, and several collaborative efforts are underway to collect the needed demonstration data. But is Data All You Need? I'll present my view of the status quo in terms of manipulation task definition, data collection, and experimental evaluation. I'll then suggest that to reach expected performance levels, it may be helpful for the community to reconsider good old fashioned engineering in terms of modularity, metrics, and failure analysis. I'll present MANIP, a potential framework for doing this that shows promise for tasks such as cable untangling, surgical suturing, and bagging. I welcome feedback: this will be the first time I present this talk and I expect it to be a bit controversial ;)

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