Adarsh Jagan Sathyamoorthy — accepted AI-conference papers

2024

AMCO: Adaptive Multimodal Coupling of Vision and Proprioception for Quadruped Robot Navigation in Outdoor Environments

Mohamed Elnoor, Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Tianrui Guan, Vignesh Rajagopal, Dinesh Manocha

IROS 2024poster

We present AMCO, a novel navigation method for quadruped robots that adaptively combines vision-based and proprioception-based perception capabilities. Our approach uses three cost maps: general knowledge map; traversability history map; and current proprioception map; which are derived from a robot…

Cited by 4 Source ↗Scholar ↗

2024

CoNVOI: Context-aware Navigation using Vision Language Models in Outdoor and Indoor Environments

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor, Anuj Zore, Brian Ichter, Fei Xia +3

IROS 2024

We present CoNVOI, a novel method for autonomous robot navigation in real-world indoor and outdoor environments using Vision Language Models (VLMs). We employ VLMs in two ways: first, we leverage their zero-shot image classification capability to identify the context or scenario (e.g., indoor corrid

Cited by 52 Source ↗Scholar ↗

2024

MIM: Indoor and Outdoor Navigation in Complex Environments Using Multi-Layer Intensity Maps

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor, Mason Russell, Jason Pusey, Dinesh Manocha

ICRA 2024poster

We present MIM (Multi-Layer Intensity Map), a novel 3D object representation for robot perception and autonomous navigation. MIMs consist of multiple stacked layers of 2D grid maps each derived from reflected point cloud intensities corresponding to a certain height interval. The different layers of…

Cited by 5 Source ↗Scholar ↗

2024

MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation

Jing Liang, Peng Gao, Xuesu Xiao, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Ming C. Lin +1

ICRA 2024poster

We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global planning using limited onboard robot perception in ma…

Cited by 10 Source ↗Scholar ↗

2024

ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments

Mohamed Elnoor, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha

RA-L 2024

We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our approach uses sensor data from a legged robot's joint encoders, force, and current sensors to measure the joint positions, fo

Cited by 26 Source ↗Scholar ↗

2024

VAPOR: Legged Robot Navigation in Unstructured Outdoor Environments using Offline Reinforcement Learning

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Mohamed Elnoor, Dinesh Manocha

ICRA 2024poster

We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. Our po…

Cited by 5 Source ↗Scholar ↗

2023

CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition

Tianrui Guan, Aswath Muthuselvam, Montana Hoover, Xijun Wang, Jing Liang, Adarsh Jagan Sathyamoorthy +2

ICCV 2023poster

We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting. Cross-source point cloud data corresponds to point sets captured by depth sensors with different accuracies or from different distances and perspectives. We address…

Cited by 7 PDF ↗code ↗Scholar ↗

2023

GrASPE: Graph Based Multimodal Fusion for Robot Navigation in Outdoor Environments

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Jing Liang, Tianrui Guan, Utsav Patel, Dinesh Manocha

RA-L 2023

We present a novel trajectory traversability estimation and planning algorithm for robot navigation in complex outdoor environments. We incorporate multimodal sensory inputs from an RGB camera, 3D LiDAR, and the robot's odometry sensor to train a prediction model to estimate candidate trajectories'

Cited by 78 Source ↗Scholar ↗

2023

VERN: Vegetation-Aware Robot Navigation in Dense Unstructured Outdoor Environments

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Tianrui Guan, Mason Russell, Damon Conover, Jason Pusey +1

IROS 2023poster

We propose a novel method for autonomous legged robot navigation in densely vegetated environments with a variety of pliable/traversable and non-pliable/untraversable vegetation. We present a novel few-shot learning classifier that can be trained on a few hundred RGB images to differentiate flora th…

Cited by 18 Source ↗Scholar ↗

2022

CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes

Adarsh Jagan Sathyamoorthy, Utsav Patel, Moumita Paul, Nithish K. Sanjeev Kumar, Yash Ajay Savle, Dinesh Manocha

RA-L 2022

We present CoMet, a novel approach for computing a group’s cohesion and using that to improve a robot’s navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians f

Cited by 24 Source ↗Scholar ↗

2022

GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha

RA-L 2022

We propose GA-Nav, a novel group-wise attention mechanism to identify safe and navigable regions in unstructured environments from RGB images. Our group-wise attention method extracts multi-scale features from each type of terrain independently and classifies terrains based on their navigability lev

Cited by 161 Source ↗code ↗Scholar ↗

2022

HTRON: Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm

Kasun Weerakoon, Souradip Chakraborty, Nare Karapetyan, Adarsh Jagan Sathyamoorthy, Amrit Bedi, Dinesh Manocha

CoRL 2022poster

We present a novel approach to improve the performance of deep reinforcement learning (DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on carefully designed dense reward functions that learn the efficient behavior in an environment. We circumvent this issue by work…

Cited by 13 Source ↗Scholar ↗

2022

TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning

Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel, Dinesh Manocha

ICRA 2022poster

We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attentio…

Cited by 82 Source ↗Scholar ↗

2022

TerraPN: Unstructured Terrain Navigation using Online Self-Supervised Learning

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Tianrui Guan, Jing Liang, Dinesh Manocha

IROS 2022poster

We present TerraPN, a novel method that learns the surface properties (traction, bumpiness, deformability, etc.) of complex outdoor terrains directly from robot-terrain interactions through self-supervised learning, and uses it for autonomous robot navigation. Our method uses RGB images of terrain s…

Cited by 62 Source ↗Scholar ↗

2021

DWA-RL: Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation among Mobile Obstacles

Utsav Patel, Nithish K Sanjeev Kumar, Adarsh Jagan Sathyamoorthy, Dinesh Manocha

ICRA 2021poster

We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot’s dynamics constrai…

Cited by 88 Source ↗Scholar ↗

2020

Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation

Jing Liang, Utsav Patel, Adarsh Jagan Sathyamoorthy, Dinesh Manocha

IJCAI 2020poster

We present a novel high fidelity 3-D simulator that significantly reduces the sim-to-real gap for collision avoidance in dense crowds using Deep Reinforcement Learning (DRL). Our simulator models realistic crowd and pedestrian behaviors, along with friction, sensor noise and delays in the simulated…

Cited by 0 Source ↗PDF ↗Scholar ↗

2020

DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors

Adarsh Jagan Sathyamoorthy, Jing Liang, Utsav Patel, Tianrui Guan, Rohan Chandra, Dinesh Manocha

ICRA 2020poster

We present DenseCAvoid, a novel algorithm for navigating a robot through dense crowds and avoiding collisions by anticipating pedestrian behaviors. Our formulation uses visual sensors and a pedestrian trajectory prediction algorithm to track pedestrians in a set of input frames and compute bounding…

Cited by 107 Source ↗Scholar ↗

2020

Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds

Adarsh Jagan Sathyamoorthy, Utsav Patel, Tianrui Guan, Dinesh Manocha

RA-L 2020

We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that arises when a robot navigates through dense scenarios and crowds. Our method senses and explicitly predicts the trajectories of pedestrians and constructs a Potential Freezing Zone (PFZ); a spatial zone where th

Cited by 87 Source ↗Scholar ↗

2019

LSwarm: Efficient Collision Avoidance for Large Swarms With Coverage Constraints in Complex Urban Scenes

Senthil Hariharan Arul, Adarsh Jagan Sathyamoorthy, Shivang Patel, Michael W. Otte, Huan Xu, Ming C. Lin +1

RA-L 2019

In this letter, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in three-dimensional urban environments while consid

Cited by 42 Source ↗Scholar ↗