ramviyas
I'm an Assistant Professor at the University of Georgia (UGA) School of Computing, working in Networked Collaborative Robotics.
University of GeorgiaUSA
ramviyas's Stars
herolab-uga/cqlite
CQLite: Coverage-biased Q-Learning Lite for Efficient Multi-Robot Exploration
herolab-uga/ros-edge-orchestration
herolab-uga/IKTBT-Release
NVIDIA-ISAAC-ROS/isaac_ros_visual_slam
Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM
shushuai3/multi-robot-localization
Simulation of a swarm of robots with range-based relative localization
interaction-lab/RE-BT-Espresso
AMR-/Conservative-Q-Improvement
Conservative Q-Improvement is a reinforcement learning method that builds a decision tree as the policy. Nodes represent abstract states, with leaves corresponding to actions to execute.
matthias-mayr/behavior-tree-policy-learning
Code for the IROS 2021 paper "Learning of Parameters in Behavior Trees for Movement Skills". In short, we combine behavior trees (BT), a motion generation and movements skills (MS) to learn robust and interpretable policies.
qcr/ros_trees
Behaviour Trees for ROS with smart data flow management
RMiyagusuku/wifi-localization
Wireless signal strength based localization using Gaussian processes and path loss models
uoip/g2opy
Python binding of SLAM graph optimization framework g2o
herolab-uga/ROS-SEAL
ROS Package for Simultaneous Exploration and Localization for Multi-Robot Applications
hasauino/rrt_exploration
A ROS package that implements a multi-robot RRT-based map exploration algorithm. It also has the image-based frontier detection that uses image processing to extract frontier points.
xuxiaoli-seu/Environment_Aware_Communications
Towards environment-aware 6G communications via channel knowledge map
JianiLi/MultiRobotsRendezvous
Use Tverberg point and centerpoint to achieve fault-tolerant multi-robot consensus
fazildgr8/ros_autonomous_slam
ROS package which uses the Navigation Stack to autonomously explore an unknown environment with help of GMAPPING and constructs a map of the explored environment. Finally, a path planning algorithm from the Navigation stack is used in the newly generated map to reach the goal. The Gazebo simulator is used for the simulation of the Turtlebot3 Waffle Pi robot. Various algorithms have been integrated for Autonomously exploring the region and constructing the map with help of the 360-degree Lidar sensor. Different environments can be swapped within launch files to generate a map of the environment.
verlab/3DSemanticMapping_JINT_2020
Repository for the paper "Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues"
vita-epfl/CrowdNav
[ICRA19] Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
Harvard-REACT/WSR-Toolbox
Index page for the WSR Toolbox
florianshkurti/csc477_fall19
verlab/hero_common
This project contributes to an open source ROS-based framework for swarm robotics. We propose an low cost, high availability swarm system that could be printed and assembled multiple times without special knowledge or hardware skills.
mjyc/awesome-hri-datasets
A curated list of publically available human-robot interaction datasets
ashblue/fluid-behavior-tree
Behavior trees for Unity3D projects. Written with a code driven approach on the builder pattern.
herolab-uga/KTBT-Release
KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multirobot Systems
miccol/ros2_bt_utils
ROS2 package containing the base classes (virtual and templates) to ease the development of some ROS2-based Behavior Trees leaf nodes
lucascoelhof/voronoi_hsi
Multi robot coverage control in non-convex environments using ROS
perseus784/Multi-Robot_Exploration
This project shows the area division process in Multi-Agent exploration using Cyclic Gradient Descent and also how Cooperative Perceptional Messages are used in V2V communication to share information among agents in about the environment.
proroklab/VectorizedMultiAgentSimulator
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
shinkansan/ros2_logitech_brio_publisher
ROS2 USB Camera node
mlherd/Dataset-of-Gazebo-Worlds-Models-and-Maps
A set of Gazebo worlds models and maps that I used for testing Navigation2