Search Results for author:
Found 7 papers, 1 papers with code
PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
The study of non-line-of-sight (NLOS) imaging is growing due to its many potential applications, including rescue operations and pedestrian detection by self-driving cars.
Density Stabilization Strategies for Nonholonomic Agents on Compact Manifolds
We relax this assumption on the ellipticity of the generator of the stochastic processes, and consider the more practical case of the stabilization problem for a swarm of agents whose dynamics are given by a controllable driftless control-affine system.
Stereo Visual Odometry with Deep Learning-Based Point and Line Feature Matching using an Attention Graph Neural Network
In this paper, we present a Stereo Visual Odometry (StereoVO) technique based on point and line features which uses a novel feature-matching mechanism based on an Attention Graph Neural Network that is designed to perform well even under adverse weather conditions such as fog, haze, rain, and snow, and dynamic lighting conditions such as nighttime illumination and glare scenarios.
Single-Shot Domain Adaptation via Target-Aware Generative Augmentation
The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural networks.
Towards Decentralized Human-Swarm Interaction by Means of Sequential Hand Gesture Recognition
In this work, we present preliminary work on a novel method for Human-Swarm Interaction (HSI) that can be used to change the macroscopic behavior of a swarm of robots with decentralized sensing and control.
Hand Gesture Recognition
Hand-Gesture Recognition
Robotics
Multiagent Systems
Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains
The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.
Robotics Multiagent Systems
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution
In this paper, we present a reinforcement learning approach to designing a control policy for a "leader" agent that herds a swarm of "follower" agents, via repulsive interactions, as quickly as possible to a target probability distribution over a strongly connected graph.
