%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Brudermuller_IJRR_2026,
                      author = {Brudermuller, Lara and Berger, G. and Jankowski, Julius and Bhattacharyya, R. and Calinon, Sylvain and Jungers, R. and Hawes, N.},
                    keywords = {robot manipulation, robotics},
                    projects = {Idiap},
         mainresearchprogram = {Human-AI Teaming},
                       title = {CC-VPSTO: Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation for Online Robot Motion Planning Under Uncertainty},
                     journal = {International Journal of Robotics Research (IJRR)},
                        year = {2026},
                         doi = {10.1177/02783649261464639},
                    abstract = {Reliable robot autonomy hinges on decision-making systems that account for uncertainty without imposing overly conservative restrictions on the robot's action space. We introduce Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation (CC-VPSTO), a real-time capable framework for generating task-efficient robot trajectories that satisfy constraints with high probability by formulating stochastic control as a chance-constrained optimisation problem. Since such problems are generally intractable, we propose a deterministic surrogate formulation based on Monte Carlo sampling, solved efficiently with gradient-free optimisation. To address bias in na{\"{\i}}ve sampling approaches, we quantify approximation error and introduce padding strategies to improve reliability. We focus on three challenges: (i) sample-efficient constraint approximation, (ii) conditions for surrogate solution validity, and (iii) online optimisation. Integrated into a receding-horizon MPC framework, CC-VPSTO enables reactive, task-efficient control under uncertainty, balancing constraint satisfaction and performance in a principled manner. The strengths of our approach lie in its generality, i.e. no assumptions on the underlying uncertainty distribution, system dynamics, cost function, or the form of inequality constraints; and its applicability to online robot motion planning. We demonstrate the validity and efficiency of our approach in both simulation and on a Franka Emika robot.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2026/Brudermuller_IJRR_2026.pdf}
}