4 citations · 22 across the 23 of their papers we have counts for
6 papers · 1 filter
AdVENTR: Autonomous Robot Navigation in Complex Outdoor Environments
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor +1
We present a novel system, AdVENTR for autonomous robot navigation in unstructured outdoor environments that consist of uneven and vegetated terrains. Our approach is general and c…
Using Lidar Intensity for Robot Navigation
Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor +1
We present Multi-Layer Intensity Map, a novel 3D object representation for robot perception and autonomous navigation. Intensity maps consist of multiple stacked layers of 2D grid…
VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor +1
We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our metho…
ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments
Mohamed Elnoor, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon +1
We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our appro…
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation
Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle +5
Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sp…
RE-MOVE: An Adaptive Policy Design for Robotic Navigation Tasks in Dynamic Environments via Language-Based Feedback
Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar +6
Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may resul…