5 citations · 6 across the 6 of their papers we have counts for
9 papers
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
Faraz Lotfi, Farnoosh Faraji, Nikhil Kakodkar +3
This paper explores leveraging large language models for map-free off-road navigation using generative AI, reducing the need for traditional data collection and annotation. We prop…
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain
Stefan Wapnick, Travis Manderson, David Meger +1
We present a reward-predictive, model-based deep learning method featuring trajectory-constrained visual attention for local planning in visual navigation tasks. Our method learns…
Multimodal dynamics modeling for off-road autonomous vehicles
Jean-François Tremblay, Travis Manderson, Aurélio Noca +2
Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. L…
Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles
Travis Manderson, Juan Camilo Gamboa Higuera, Stefan Wapnick +4
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot…
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
Travis Manderson, Stefan Wapnick, David Meger +1
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual…
One-Shot Informed Robotic Visual Search in the Wild
Karim Koreitem, Florian Shkurti, Travis Manderson +3
We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that…