Anushri Dixit

TL;DR: In this paper, the authors proposed an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time, which relies on rapid uncertainty-aware mapping and traversability evaluation, tail risk assessment using the Conditional Value-at-Risk (CVaR), and efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based predictive control.

TL;DR: An algorithm to follow waypoints and discuss its feasibility and completion in finite time is proposed and the policies obtained using EVaR are compared with those obtained using another common coherent risk measure, Conditional Value-at-Risk, via numerical experiments for a 2D system.

TL;DR: NeBula as mentioned in this paper is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states).

TL;DR: In this paper , an adaptive conformal prediction algorithm is proposed for motion planning among dynamic agents using an online data stream, which uses delayed agent observations to obtain uncertainty sets for multistep-ahead predictions with probabilistic coverage.

TL;DR: The paper introduces the autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy), an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states).