State-of-the-art radiotherapy machines with integrated magnetic resonance (MR) imaging, known as MR-Linacs, provide the capability to track tumors in real time. This capability aids delivery of precise irradiation in the presence of patient motion, such as breathing, by adjusting the radiation beam. However, current solutions rely solely on geometric tracking without closing the loop by considering the actual endpoint-the delivered radiation (here called dose). Real-time dose-based adaptation within a single session remains highly challenging due to the immense dimensionality of the problem. To overcome this, we have developed a radiotherapy simulator and propose a novel reinforcement learning (RL)-based approach for real-time adaptive radiotherapy using 2D fluence, as a surrogate to 3D dose. To our knowledge, this is the first application of RL in real-time adaptive radiotherapy. Our in-silico experiments showed the feasibility of using RL to close the feedback loop, dynamically adapting to patient motion and minimizing discrepancies between delivered and intended dose in clinical cases. Our approach introduces a new treatment delivery paradigm, enabling delivery based on a reference fluence and motion without predefined machine settings.

In environments like offices, the duration of a robot’s navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Temporal Logic (MITL) – a rich robot task specification language that allows us to capture explicit time requirements. Our objective is to find a strategy that maximizes the temporal robustness of the robot’s MITL task. As the first step towards a solution, we define a Mixed-integer linear programming approach to solving the task planning problem over a Varying Weighted Transition System, where navigation durations are deterministic but vary depending on the time of day. Then, we apply this planner to optimize for MITL temporal robustness in Markov Decision Processes, where the navigation durations between physical locations are uncertain, but the time-dependent distribution over possible delays is known. Finally, we develop a receding horizon planner for Markov Decision Processes that preserves guarantees over MITL temporal robustness. We show the scalability of our planning algorithms in simulations of robotic tasks.

Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatio-temporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and preferences in the Real-Time Rapidly Exploring Random Tree (RT-RRT*) motion planning algorithm in an environment with dynamic obstacles. We propose a cost function that guides the algorithm towards the asymptotically most robust solution, i.e. a plan that maximally adheres to the STL specification. In experiments, we applied our method to a social navigation case, where the STL specification captures spatio-temporal preferences on how a mobile robot should avoid an incoming human in a shared space. Our results show that our approach leads to plans adhering to the STL specification, while ensuring efficient cost computation.

This paper is interested in formalizing human trajectories in human-robot encounters. Inspired by robot navigation tasks in human-crowded environments, we consider the case where a human and a robot walk towards each other, and where humans have to avoid colliding with the incoming robot. Further, humans may describe different be-haviors, ranging from being in a hurry/minimizing completion time to maximizing safety. We propose a decision tree-based algorithm to extract STL formulae from multi-label data. Our inference algorithm learns STL specifications from data containing multiple classes, where instances can be labelled by one or many classes. We base our evaluation on a dataset of trajectories collected through an online study reproducing human-robot encounters.

Two people walking towards each other in a colliding course is an everyday problem of human-human interaction. In spite of the different environmental and individual factors that might jeopardise successful human trajectories, people are generally skilled at avoiding crashing into each other. However, it is not clear if the same strategies will apply when a human is in a colliding course with a robot, nor which (if any) robot-related factors will influence the human's decision to swerve or not. In this work, we present the results of an online study where participants walked towards a virtual robot that differed in terms of anthropomorphism and perceived autonomy, and had to decide whether to swerve, or continue straight. The experiment was inspired by the game-theoretic game of chicken. We found that people performed more swerving actions when they believed the robot to be teleoperated by another participant. When they swerved, they also swerved closer to the robot with high levels of human-likeness, and farther away from the robot with low anthropomorphism score, suggesting a higher uncertainty about the mechanical-looking robot's intentions. These results are discussed in the context of socially-aware robot navigation, and will be used to design novel algorithms for robot trajectories that take robot-related differences into account.

In this paper, we are interested in formalizing human trajectories in human-robot encounters. We consider a particular case where a human and a robot walk towards each other. A question that arises is whether, when, and how humans will deviate from their trajectory to avoid a collision. These human trajectories can then be used to generate socially acceptable robot trajectories. To model these trajectories, we propose a data-driven algorithm to extract a formal specification expressed in Signal Temporal Logic with probabilistic predicates. We evaluated our method on trajectories collected through an online study where participants had to avoid colliding with a robot in a shared environment. Further, we demonstrate that probabilistic STL is a suitable formalism to depict human behavior, choices and preferences in specific scenarios of social navigation.

Optimizing the productivity of Flexible Manufacturing Systems requires online scheduling to ensure that the timing constraints due to complex interactions between modules are satisfied. This work focuses on optimizing a ranking metric such that the online scheduler locally (i.e., per product) chooses an option that yields the highest productivity in the long term. In this paper, we focus on the scheduling of a re-entrant Flexible Manufacturing System, more specifically a Large Scale Printer capable of printing hundreds of sheets per minute. The system requires an online scheduler that determines for each sheet when it should enter the system, be printed for the first time, and when it should return for its second print. We have applied genetic programming, a hyper-heuristic, to heuristically find good ranking metrics that can be used in an online scheduling heuristic. The results show that metrics can be tuned for different job types, to increase the productivity of such systems. Our methods achieved a significant reduction in the jobs' makespan.