Andrzej Wąsowski

A controller -- a software module managing hardware behavior -- is a key component of a typical robot system. While control theory gives safety guarantees for standard controller designs, the practical implementation of controllers in software introduces complexities that are often overlooked. Controllers are often designed in continuous space, while the software is executed in discrete space, undermining some of the theoretical guarantees. Despite extensive research on control theory and control modeling, little attention has been paid to the implementations of controllers and how their theoretical guarantees are ensured in real-world software systems. We investigate 184 real-world controller implementations in open-source robot software. We examine their application context, the implementation characteristics, and the testing methods employed to ensure correctness. We find that the implementations often handle discretization in an ad hoc manner, leading to potential issues with real-time reliability. Challenges such as timing inconsistencies, lack of proper error handling, and inadequate consideration of real-time constraints further complicate matters. Testing practices are superficial, no systematic verification of theoretical guarantees is used, leaving possible inconsistencies between expected and actual behavior. Our findings highlight the need for improved implementation guidelines and rigorous verification techniques to ensure the reliability and safety of robotic controllers in practice.

This document presents the outcomes of the Dagstuhl Seminar “Roadmap for Responsible Robotics,” held in September 2023 at the Leibniz Center for Informatics, Schloss Dagstuhl, Germany. The seminar brought together researchers from the fields of robotics, computer science, social and cognitive sciences, and philosophy with the aim of charting a path toward improving responsibility in robotic systems. Through intensive interdisciplinary discussions centered on the various values at stake as robotics increasingly integrates into human life, the participants identified key priorities to guide future research and regulatory efforts. The resulting road map outlines actionable steps to ensure that robotic systems coevolve with human societies, promoting human agency and humane values rather than undermining them. Designed for diverse stakeholders—researchers, policy makers, industry leaders, practitioners, nongovernmental organizations (NGOs), and civil society groups—this road map provides a foundation for collaborative efforts toward responsible robotics.

We present Uppaal Coshy, a tool for automatic synthesis of a safety strategy -- or shield -- for Markov decision processes over continuous state spaces and complex hybrid dynamics. The general methodology is to partition the state space and then solve a two-player safety game, which entails a number of algorithmically hard problems such as reachability for hybrid systems. The general philosophy of Uppaal Coshy is to approximate hard-to-obtain solutions using simulations. Our implementation is fully automatic and supports the expressive formalism of Uppaal models, which encompass stochastic hybrid automata. The precision of our partition-based approach benefits from using finer grids, which however are not efficient to store. We include an algorithm called Caap to efficiently compute a compact representation of a shield in the form of a decision tree, which yields significant reductions.

Data anonymization methods gain legal importance as data collection and analysis are expanding dramatically in data management and statistical research. Yet applying anonymization, or understanding how well a given analytics program hides sensitive information, is non-trivial. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug is equipped with approximate Bayesian inference methods (such as Markov Chain Monte Carlo), and an exact Bayesian inference method based on multivariate Gaussian distributions. This paper introduces a privacy risk analysis engine based on Gaussian mixture models that combines exact and approximate inference. It extends the multivariate Gaussian engine by supporting exact inference in programs with continuous and discrete distributions as well as if-statements. Furthermore, the engine allows for approximating attacker knowledge that is not normally distributed. We evaluate the method by analyzing privacy risks in programs to release public statistics, differential privacy mechanisms, randomized response and attribute generalization. Finally, we show that our engine can be used to analyze programs involving thousands of sensitive records.

Tabular reinforcement learning methods cannot operate directly on continuous state spaces. One solution to this problem is to partition the state space. A good partitioning enables generalization during learning and more efficient exploitation of prior experiences. Consequently, the learning process becomes faster and produces more reliable policies. However, partitioning introduces approximation, which is particularly harmful in the presence of nonlinear relations between state components. An ideal partition should be as coarse as possible, while capturing the key structure of the state space for the given problem. This work extracts partitions from the environment dynamics by symbolic execution. We show that symbolic partitioning improves state space coverage with respect to environmental behavior and allows reinforcement learning to perform better for sparse rewards. We evaluate symbolic state space partitioning with respect to precision, scalability, learning agent performance and state space coverage for the learned policies.