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Found 9 papers, 0 papers with code
Explore until Confident: Efficient Exploration for Embodied Question Answering
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question.
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification
The dramatic increase of autonomous systems subject to variable environments has given rise to the pressing need to consider risk in both the synthesis and verification of policies for these systems.
Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures
This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles.
Distributionally Robust Model Predictive Control with Total Variation Distance
This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets.
Risk-Averse Stochastic Shortest Path Planning
We consider the stochastic shortest path planning problem in MDPs, i. e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem.
Risk-Sensitive Motion Planning using Entropic Value-at-Risk
We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles.
