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Found 64 papers, 18 papers with code
Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems
Next, we prove that symmetry in the underlying dynamics and running costs leads to an MDP homomorphism, a mapping that allows a policy trained on a lower-dimensional "quotient" MDP to be lifted to an optimal tracking controller for the original system.
Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance
We demonstrate the capabilities of an attention-based end-to-end approach for high-speed quadrotor obstacle avoidance in dense, cluttered environments, with comparison to various state-of-the-art architectures.
Medical Image Data Provenance for Medical Cyber-Physical System
This approach is suitable for a range of applications, including telemedicine, the Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, providing a reliable means to maintain data provenance in diagnostic settings utilizing medical images or videos.
Almost Global Asymptotic Trajectory Tracking for Fully-Actuated Mechanical Systems on Homogeneous Riemannian Manifolds
In summary, we propose a unified, intrinsic controller guaranteeing almost global asymptotic trajectory tracking for fully-actuated mechanical systems evolving on a broad class of manifolds.
LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control
Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori.
Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems
In this paper, we argue that adapting the trajectory generation component keeping the controller fixed can improve trajectory tracking for quadrotor systems experiencing drag forces.
EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields
We present EvDNeRF, a pipeline for generating event data and training an event-based dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid and non-rigid deformations that may be too fast to capture with a standard camera.
Asynchronous Perception-Action-Communication with Graph Neural Networks
A fundamental challenge in decentralized PAC systems is to decide what information to communicate with the neighboring robots and how to take actions while utilizing the information shared by the neighbors.
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense
In this work, we study the problem of decentralized multi-agent perimeter defense that asks for computing actions for defenders with local perceptions and communications to maximize the capture of intruders.
Reinforcement Learning for Agile Active Target Sensing with a UAV
Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions.
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration.
Vision-based Perimeter Defense via Multiview Pose Estimation
Previous studies in the perimeter defense game have largely focused on the fully observable setting where the true player states are known to all players.
Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks
We consider the problem of finding decentralized strategies for multi-agent perimeter defense games.
Learning Fashion Compatibility from In-the-wild Images
In this work, we propose to learn representations for compatibility prediction from in-the-wild street fashion images through self-supervised learning by leveraging the fact that people often wear compatible outfits.
RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics
Because it is difficult to develop first principle models of cars driven by humans, there is great potential for using data driven approaches in developing traffic dynamical models.
Dynamic Defender-Attacker Blotto Game
The game terminates with the attacker's win if any location has more attacker robots than defender robots at any time.
Learning Connectivity-Maximizing Network Configurations
In this letter we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots.
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori.
Semi-Supervised Visual Representation Learning for Fashion Compatibility
We consider the problem of complementary fashion prediction.
Ranked #2 on
Recommendation Systems
on Polyvore
Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
Semantic maps represent the environment using a set of semantically meaningful objects.
Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
We demonstrate the performance of our GNN-based learning approach in a scenario of active target tracking with large networks of robots.
Learning Connectivity for Data Distribution in Robot Teams
To overcome this challenge, we propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN).
Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
We are able to demonstrate the scalability of our methods for a large number of robots by employing a graph neural network (GNN) to parameterize policies for the robots.
Neurosymbolic Transformers for Multi-Agent Communication
We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication.
Place Recognition in Forests with Urquhart Tessellations
In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest.
PennSyn2Real: Training Object Recognition Models without Human Labeling
Scalable training data generation is a critical problem in deep learning.
Resilient Task Allocation in Heterogeneous Multi-Robot Systems
For a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks.
Multiagent Systems Robotics
Feedback Enhanced Motion Planning for Autonomous Vehicles
In this work, we address the motion planning problem for autonomous vehicles through a new lattice planning approach, called Feedback Enhanced Lattice Planner (FELP).
Robotics
TLIO: Tight Learned Inertial Odometry
We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates.
Graph Neural Networks for Motion Planning
This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems.
SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory
This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping.
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding
Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
The approach combines an offline-trained fully-connected neural network with an online primal active set solver.
PST900: RGB-Thermal Calibration, Dataset and Segmentation Network
In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques.
Ranked #16 on
Thermal Image Segmentation
on PST900
Mine Tunnel Exploration using Multiple Quadrupedal Robots
Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees of autonomy and agility.
Robotics
Perimeter-defense Game on Arbitrary Convex Shapes
Secondly, we solve the two vs. one game to introduce a cooperative pincer maneuver, where a pair of defenders team up to capture an intruder that cannot be captured by either one of the defender individually.
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Another approach explored in the field relies on an ad-hoc linearization (in terms of N) of the triplet loss that introduces class centroids, which must be optimized using the whole training set for each mini-batch - this means that a naive implementation of this approach has run-time complexity O(N^2).
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks
Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection.
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications.
Robotics
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image.
Decentralization of Multiagent Policies by Learning What to Communicate
Effective communication is required for teams of robots to solve sophisticated collaborative tasks.
Robotics
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
In this work, we propose a method that combines unsupervised deep learning predictions for optical flow and monocular disparity with a model based optimization procedure for instantaneous camera pose.
Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association
We present a cheap, lightweight, and fast fruit counting pipeline that uses a single monocular camera.
Robotics
Towards Search-based Motion Planning for Micro Aerial Vehicles
Search-based motion planning has been used for mobile robots in many applications.
Robotics
Stochastic 2-D Motion Planning with a POMDP Framework
Motion planning is challenging when it comes to the case of imperfect state information.
Robotics
COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
A deep reinforcement learning solution is developed for a collaborative multiagent system.
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements
We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera.
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures.
Spin-Boson Model to Demonstrate Quantum Tunneling in Biomolecules using IBM Quantum Computer
Efficient simulation of quantum mechanical problems can be performed in a quantum computer where the interactions of qubits lead to the realization of various problems possessing quantum nature.
Quantum Physics
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these methods do not scale well.
Distributed Optimization
Multi-agent Reinforcement Learning
+2
Human Motion Capture Using a Drone
Current motion capture (MoCap) systems generally require markers and multiple calibrated cameras, which can be used only in constrained environments.
Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion
We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images.
The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
Event based cameras are a new passive sensing modality with a number of benefits over traditional cameras, including extremely low latency, asynchronous data acquisition, high dynamic range and very low power consumption.
Robotics
Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering
Many accurate and efficient methods exist that address this problem but most assume that the occupancy states of different elements in the map representation are statistically independent.
Robotics
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment.
Robotics
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with micro aerial vehicles in which it is difficult to use high quality sensors and pow- erful processors because of constraints on size and weight.
Robotics
Memory Augmented Control Networks
The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning.
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring.
Ranked #4 on
Homography Estimation
on S-COCO
Pose-Aware Person Recognition
Person recognition methods that use multiple body regions have shown significant improvements over traditional face-based recognition.
Neural Network Memory Architectures for Autonomous Robot Navigation
This work is a first thorough study of memory structures for deep-neural-network-based robot navigation, and offers novel tools to train such networks from supervision and quantify their ability to generalize to unseen scenarios.
DimensionApp : android app to estimate object dimensions
In this project, we develop an android app that uses on computer vision techniques to estimate an object dimension present in field of view.
Visual Phrases for Exemplar Face Detection
Contrary to traditional approaches that model face variations from a large and diverse set of training examples, exemplar-based approaches use a collection of discriminatively trained exemplars for detection.
Clustering by transitive propagation
We present a global optimization algorithm for clustering data given the ratio of likelihoods that each pair of data points is in the same cluster or in different clusters.
