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The rapid proliferation of artificial intelligence (A.I.) and large language models (LLMs) is revolutionizing our world. However, as these systems increasingly find real-world applications in controlling physical systems—such as autonomous robots, self-driving cars, and other critical infrastructure—their potential to cause harm has escalated dramatically. This is due to large error rates, lack of robustness, hallucinations, as well as a new LLM attack known as jailbreaking. Ensuring safety in safety critical contexts requires a paradigm shift from traditional A.I. development toward robust safety mechanisms. In this talk, I will explore how ideas from control theory can provide rigorous tools and frameworks for developing safety filters tailored towards control systems with deep learning in the loop and LLM-controlled robots, including VLA-controlled robots. By leveraging tools such as integrated quadratic constraints, temporal logic synthesis, and control barrier functions, I will address how our community can play a crucial role in designing A.I. safety systems that effectively mitigate risks while preserving the utility and adaptability of A.I. in real world applications.
Recent advances in large foundation models, such as large language models (LLMs) and diffusion models, have demonstrated impressive capabilities. However, to truly align these models with user feedback or maximize real-world objectives, it is crucial to exert control over the decoding processes, in order to steer the distribution of generated output. In this talk, we will explore methods and theory for controlled generation within LLMs and diffusion models. We will discuss various modalities or achieving this control, focusing on applications such as alignment of LLM, accelerated inference, transfer learning, and diffusion-based optimizer.
Biological cells are complex dynamic systems in which sensing, control, and actuation are orchestrated by networks of active molecules with coupled functions. Cells not only detect and process information, but also leverage that information to direct the assembly of physical structures, such as scaffolds, membranes, and organelles. A productive approach to understanding the design principles governing these multicomponent systems involves the constructive, bottom-up synthesis of biomolecules capable of performing dynamic tasks. Many challenges emerge when building such systems, such as parametric uncertainty, unintended interactions, and the breakdown of signaling modularity, which can be better understood by synthesis efforts outside the cell. I will discuss these challenges in the context of in vitro synthetic biology, which offers a simplified environment for constructing coupled biochemical reaction networks and self-assembling systems. I will begin by describing methods to design artificial nucleic acids (DNA and RNA) that drive the creation of signal generators and components that self-assemble into a variety of structural elements, like filamentous crystals and amorphous condensates. Next, I will explore how these signaling networks and self-assembled structures can be interconnected via the design of nucleic acid sequences, using computational tools to program their interactions. I will then discuss how pulse generators and oscillator circuits can regulate the formation and dissolution of self-assembled physical structures. I will also describe how these techniques have enabled the creation of dynamic, artificial organelles within living cells. These advances contribute to our broader goal of controlling physical matter through biomolecular reactions, paving the way for the development of intelligent biological materials that can sense and make decisions through embedded control programs.
Mathematical models are indispensable for system design. Until recently, one could argue that this was also true for robot control design. Recent advances in AI are calling this basic assumption into question: Why not learn a control strategy directly on a physical system, without any prior knowledge provided by mathematical models, just like animals – including human beings -- do? Or less ambitiously, only use mathematical models for simulating physical systems, but otherwise do control design directly on a robot, either real or simulated. In this talk, I will present my first-hand experiences in model-based control design, ranging from soccer playing robots to fleets of autonomous mobile robots in warehouses to acrobatic flying robots. I will conclude with a live demonstration of CyberRunner, an opensource machine learning platform developed at ETH based on the popular “Labyrinth” dexterity game in which a ball is guided through a maze while avoiding traps. CyberRunner learns through experience and can beat any human with only 1 hour of training and without any prior knowledge.
Policy Optimization methods enjoy wide practical use in Reinforcement Learning (RL) for applications ranging from robotic manipulation to game-playing, partly because they are easy to implement, and require only black-box access to the underlying model. This talk focuses on recent developments in policy optimization (a gradient-based approach for feedback control design) popularized by its success in RL. We describe theoretical results on the optimization landscape, global convergence, and sample complexity of policy optimization in several canonical continuous control problems, despite their nonconvexity in policy parameters, by exploiting structural properties such as the Polyak-Lojasiewicz (or gradient dominance) condition. This line of work attempts to bring control theory and RL closer, and has helped advance two recent trends: (1) use of control problems as benchmarks for less-understood RL algorithms, and (2) theoretically-sound use of RL-style methods in control.
In an era where Artificial Intelligence (AI) is often seen as a universal solution for any complex problem, this presentation offers a critical examination of its role in the field of automatic control. To be concrete, I will focus on Optimal Control techniques, navigating through its history and addressing the evolution from its traditional model-based roots to the emerging data-driven methodologies empowered by AI.
The presentation will delve into how the theoretical underpinnings of Optimal Control have been historically aligned with computational capabilities, and how this alignment has shifted over the years. This juxtaposition of theory and computation motivates a deeper investigation into the diminishing relevance of certain traditional control methods amidst the AI revolution. We will critically examine scenarios where AI-driven approaches could outperform classical methods, as well as cases where the hype surrounding AI overshadows its actual utility.
The talk will conclude with a nuanced view of state-of-the-art optimal control methods in practical applications including self-driving cars, advanced robotics and energy efficient systems. From this perspective, we will identify and explore future potential directions for the field, including the design of learning control architectures which seamlessly integrate predictive capabilities at every level, focusing on systems that can autonomously refine their performance over time through continuous learning and interaction with their environment.
The convergence of physical and digital systems in modern engineering applications has inevitably led to closed-loop systems that exhibit both continuous-time and discrete-time dynamics. These closed-loop architectures are modeled as hybrid dynamical systems, prevalent across various technological domains, including robotics, power grids, transportation networks, and manufacturing systems. Unlike traditional “smooth” ordinary differential equations or discrete-time recursions, solutions to hybrid dynamical systems are generally discontinuous, lack uniqueness, and have convergence and stability properties that are defined with respect to complex sets. Therefore, effectively designing and controlling such systems, especially under disturbances and uncertainty, is crucial for the development of autonomous and efficient data-driven engineering systems capable of achieving adaptive and self-optimizing behaviors. In this talk, I will delve into recent advancements in the analysis and design of feedback controllers that can achieve such properties in complex scenarios via the synergistic use of adaptive “seeking” dynamics, robust hybrid control, and decision-making algorithms. These controllers can be systematically designed and analyzed using modern tools from hybrid dynamical systems theory, which facilitate the incorporation of "exploration” and “exploitation" behaviors within complex closed-loop systems via multi-time scale tools and perturbation theory. The proposed methodology leads to a family of provably stable and robust algorithms suitable for solving model-free feedback stabilization and decision-making problems in single-agent and multi-agent systems for which smooth feedback solutions fall short.
