We introduce Conformal Decision Theory, a framework for producing safe autonomous
decisions despite imperfect machine learning predictions. Examples of such decisions are …

Ground robots navigating in complex, dynamic environments must compute collision-free
trajectories to avoid obstacles safely and efficiently. Nonconvex optimization is a popular …

Autonomous mobile robots require predictions of human motion to plan a safe trajectory that
avoids them. Because human motion cannot be predicted exactly, future trajectories are …

This paper addresses the problem of safety-critical control of autonomous robots,
considering the ubiquitous uncertainties arising from un-modeled dynamics and noisy …

Safety in the face of uncertainty is a key challenge in robotics. We introduce a real-time
capable framework to generate safe and task-efficient robot motions for stochastic control …

We present AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion), a novel
navigation approach to address holonomic robot collision avoidance when the degree of …

Intelligent connected vehicles, as the focus of the global automotive industry, are currently at
a critical stage of large-scale commercialization. However, during the development process …

To safely and efficiently solve motion planning problems in multi-agent settings, most
approaches attempt to solve a joint optimization that explicitly accounts for the responses …

In this paper, we present a probabilistic and unconstrained model predictive control
formulation for robot navigation under uncertainty. We present (1) a closed-form …