Risk-aware Robot Safety via Control in Belief Space and Beyond
Time: Fri 2026-06-05 14.00
Location: D3, Lindstedtsvägen 5, Stockholm
Language: English
Subject area: Computer Science
Doctoral student: Matti Vahs , Robotik, perception och lärande
Opponent: Associate Professor Nikolay Atanasov,
Supervisor: Jana Tumova, Robotik, perception och lärande
QC 20260508
Abstract
Robotic systems must operate safely despite noisy measurements, partial observability, and imperfect models of their dynamics. These sources of uncertainty fundamentally challenge how safety can be ensured, as classical control methods typically assume exact knowledge of the system state and model. This thesis develops a principled foundation for robot safety under uncertainty by designing control strategies directly in belief space, a representation that captures how uncertainty evolves through stochastic motion and observation processes. Working in belief space enables safety and performance requirements to be expressed in terms of the robot's probabilistic description of the state, rather than an assumed deterministic one.
Viewing autonomy through this lens enables explicit reasoning about risk and information. Safety specifications can be expressed as risk constraints on the belief, allowing the controller to account for low-probability but safety-critical tail events. At the same time, the belief representation enables the robot to reason about how observations can reduce uncertainty, and to actively steer toward regions where uncertainty can be reduced more effectively. A key contribution of this thesis is the formalization of control certificates such as Control Barrier Functions and Control Lyapunov Functions in belief spaces. These certificates provide formal safety and convergence guarantees directly in belief space while admitting computationally tractable controllers.
The thesis further extends these insights beyond belief space control. It interprets components of robot control such as trajectory planning and certificate generation as dynamical processes whose evolution can themselves be subject to invariance principles. This broader viewpoint leads to new formulations that treat trajectory generation and safety verification within a unified dynamical-systems framework.
Together, these contributions advance the ability of autonomous systems to reason about and act safely under uncertainty, supporting reliable deployment in real-world environments.