4 citations · 15 across the 15 of their papers we have counts for
12 papers · 1 filter
Efficient Navigation in Unknown Indoor Environments with Vision-Language Models
D. Schwartz, K. Kondo, J. P. How
We present a novel high-level planning framework that leverages vision-language models (VLMs) to improve autonomous navigation in unknown indoor environments with many dead ends. T…
Distribution Estimation for Global Data Association via Approximate Bayesian Inference
Yixuan Jia, Mason B. Peterson, Qingyuan Li +2
Global data association is an essential prerequisite for robot operation in environments seen at different times or by different robots. Repetitive or symmetric data creates signif…
Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
Yi-Hsuan Hsiao, Andrea Tagliabue, Owen Matteson +4
Aerial insects exhibit highly agile maneuvers such as sharp braking, saccades, and body flips under disturbance. In contrast, insect-scale aerial robots are limited to tracking non…
DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown Environments
Kota Kondo, Mason Peterson, Nicholas Rober +5
This paper introduces DYNUS, an uncertainty-aware trajectory planner designed for dynamic unknown environments. Operating in such settings presents many challenges -- most notably,…
REVISE: Robust Probabilistic Motion Planning in a Gaussian Random Field
Alex Rose, Naman Aggarwal, Christopher Jewison +1
This paper presents Robust samplE-based coVarIance StEering (REVISE), a multi-query algorithm that generates robust belief roadmaps for dynamic systems navigating through spatially…
SDP Synthesis of Maximum Coverage Trees for Probabilistic Planning under Control Constraints
Naman Aggarwal, Jonathan P. How
The paper presents Maximal Covariance Backward Reachable Trees (MAXCOVAR BRT), which is a multi-query algorithm for planning of dynamic systems under stochastic motion uncertainty…