Aerial robot estimates human pose and path using dynamic classifier selection.
problem Estimating human pose and trajectory from aerial video.
method Dynamic classifier selection architecture; perspective correction; HOG and CNN features; 64 pose-viewpoint classes.
result Dynamic classifier selection improves efficiency and accuracy.
Bayesian uncertainty from deep learning improves robot safety in unknown areas.
problem Improving robot safety in unknown or dangerous environments.
method Bayesian approximations of uncertainty from deep learning in a robot planner.
result Incorporating uncertainty leads to 18% less risky paths.
Hybrid RL combines simulated and real data for robust autonomous flight.
problem Challenges in training deep RL models for real-world robotic tasks.
method Combines real-world and simulated data to improve generalization.
result Quadrotor avoids collisions using only a monocular camera.
Paper presents a new port-Hamiltonian model for vehicle manipulators.
problem Complex mechanical systems' energy flow and conservation.
method Derives port-Hamiltonian dynamics from Hamiltonian reduction theory.
result Establishes mathematical equivalence with existing formulations.
New method constructs geometric flat outputs for robotic systems using symmetry.
problem Finding flat outputs for arbitrary robotic systems remains an open question.
method Employing symmetry directly to construct a flat output.
result Demonstrated geometric flat outputs for various robotic systems.
Paper proposes Q-learning for efficient aerial BS placement to improve fairness in mobile networks.
problem Optimal placement of aerial base stations to enhance fairness in a dynamic user mobility environment.
method Reinforcement learning approach to solve the NP-hard problem of 3D placement.
result Simulation results show increased fairness among users with a reasonable computing time and solution close to optimal.
New algorithms allow multiple robots to search efficiently without central coordination.
problem Efficiently locating targets in an unknown environment with multiple robots.
method SPATS and LATSI algorithms for asynchronous multi-agent active search.
result Multiple robots can independently make data-collection decisions without a central coordinator.
MRCNet tackles crowd counting and density mapping in aerial imagery.
problem Accurate crowd counting and density estimation in aerial imagery.
method MRCNet is a novel encoder-decoder CNN that combines VGG-16 with FPN-inspired lateral connections.
result MRCNet outperforms state-of-the-art methods in aerial and CCTV-based crowd counting.
DALES offers a large annotated aerial LiDAR dataset for 3D deep learning.
problem Lack of large-scale annotated aerial LiDAR datasets for deep learning.
method Collection and annotation of over half a billion hand-labeled points from an ALS scanner.
result DALES is the most extensive publicly available ALS data set with improved resolution and coverage.
This paper surveys imitation learning methods and challenges.
problem Difficulty in programming AI systems in complex environments.
method Learning from expert demonstrations to adapt behaviors.
result Overview of recent advances and emerging areas in IL.
This paper classifies typhoon damage features using aerial photography.
problem Typhoon damages and slow recovery times.
method Aerial photography for classification of eight classes including land covers and disaster areas.
result Visualize and explain typhoon disaster features using convolutional activation maps.
Monitoring of disasters is crucial for mitigating their effects on the environment and human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors that produce aerial photos of the areas of interest. A modern technique for recognition of events based on aerial pho…
Paper proposes a method to improve building extraction from aerial images by adapting CNN models.
problem Limited generalization of CNN-based segmentation models for unseen images.
method Combines domain transfer and adversarial attack concepts to adapt input images to target images.
result Improves overall IoU and outperforms other methods in cross-dataset experiments.
Paper proposes efficient UAV placement for aerial base stations.
problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.
method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.
result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.
A new algorithm finds optimal solutions for constrained decision processes.
problem Optimizing state-value functions with constraints in CMDPs.
method Gradient-Aware Search (GAS) exploiting PWLC structure.
result GAS converges faster and more reliably than existing methods.
Study uses aerial or satellite imagery to improve land price prediction in Thailand.
problem Limited accuracy of traditional land price prediction methods.
method Proposes a similarity-based asset valuation model using Siamese-inspired Neural Network and ensembles deep learning and tree-based models.
result Achieves an AUC of approximately 0.81, outperforming traditional methods.
Deep learning classifies land use from high-resolution aerial imagery.
problem Variations in land features in aerial imagery due to sensor settings and context.
method Used deep convolutional neural networks to classify land use from VHR orthophoto mosaics.
result Deep learning can accurately classify land use from high-resolution visible band multispectral imagery.
This paper evaluates conformal prediction for aerial image classification in challenging environments.
problem Challenging aerial image classification in data-scarce, unconstrained environments.
method Conformal prediction applied to pretrained models (MobileNet, DenseNet, ResNet) with limited labeled data.
result Conformal prediction can provide valuable uncertainty estimates even with small labeled samples.
Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls. Intuitively, search algorithms may increase efficiency by collecting aggregate measurements summarizing large contiguous regions. However…
A scheme for UAVs to borrow spectrum from terrestrial networks for disaster relief.
problem Spectrum shortage in UAV networks for critical missions.
method Hierarchical spectrum sharing model with relaying UAVs and reinforcement learning.
result Autonomous spectrum management improves QoS and prolongs UAV lifetime.
UAVs use RL to outperform a greedy shooter in 2D engagements.
problem Optimizing UAV pursuit-evasion strategies in 2D environments.
method Reinforcement learning applied to a greedy shooter strategy in a 2D aerial engagement.
result Deep Q-learning UAV outperforms greedy shooter UAV in simulations.
Geometric families of low-rank covariances improve flexibility and tractability in high dimensions.
problem Interpolating and identifying covariance matrices in high dimensions with limited data.
method Differential geometric construction of low-rank covariance families, interpolation on manifolds, and distance minimization for identification.
result Differential geometric covariance families offer significant flexibility and computational tractability.
UAV uses RL to navigate, map, and detect targets in unknown environments.
problem Optimizing UAV trajectory for accurate mapping and target detection in unknown environments.
method Formulated as an MDP, UAV uses RL to infer navigation policy.
result UAV autonomously explores high target detection areas while reconstructing the environment.
This work reveals a new scaling law for optimal design of multirotor aerial vehicles.
problem Designing optimal configurations for fully-actuated multirotor aerial vehicles.
method Formulated on the product manifold of Projective Lines \RP^2^N, minimizing a coordinate-invariant Log-Volume isotropy metric.
result The topology of the global optima is governed by the symmetry of the chassis, leading to a N-5 Scaling Law.
Automates decision-making for human operators managing multiple robots.
problem Limited human operator attention when controlling multiple robots.
method Learned model of user preferences from easy settings to automatically identify the most critical robot.
result Automated decision-making can assist human operators in managing more robots than their attention allows.
Bayesian fusion improves radar target recognition for UAVs.
problem Improving radar target recognition for UAVs using multistatic radar configurations.
method Proposes a fully Bayesian RATR framework using Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
result Empirical results show that the OBF method significantly enhances classification accuracy compared to other fusion methods and single radar configurations.
Robotics: Rolling robots on a moving platform can be controlled.
problem Controlling the motion of rolling robots atop a moving platform.
method Developed a mathematical model and demonstrated simulations.
result Platform acceleration can control robot's heading and motion.
Robots learn to handle complex tasks creatively using DRL.
problem Robotic manipulation challenges and intelligence.
method Designing challenging manipulation tasks, applying DRL for robot training.
result Robots exhibit creative and non-intuitive problem-solving.
ROBEL platform accelerates reinforcement learning with low-cost robots.
problem Accelerating reinforcement learning research in robotics.
method Open-source platform of cost-effective robots for real-world reinforcement learning.
result Robots D'Claw and D'Kitty facilitate learning dexterous manipulation and agile locomotion tasks.
New method improves smoothness of robot learning.
problem Jerky motion patterns on real robots from Deep RL exploration.
method Adapting state-dependent exploration to Deep RL algorithms with gSDE.
result Improved exploration and performance on real robots.
Robots learn to navigate rough terrain using reinforcement learning.
problem Generalizing robot behavior to new, unseen rough terrains.
method PPMC RL Training Algorithm
result Robots achieve 100% success rate in learning new rough terrain maps.
A new framework for robot block-stacking tasks using causal probabilistic models.
problem Robots fail outside controlled environments due to uncertainty and lack of explicit design for all scenarios.
method Causal probabilistic framework combining causal models and probabilistic representations of noise.
result Robots can perceive, reason about, and explain their environment for block-stacking tasks.
This research evaluates learning models for bionic robots, focusing on transfer function identification.
problem Developers need guidance on selecting and constructing transfer functions for bionic robots.
method Comprehensive evaluation strategy including data collection, learning model selection, comparative analysis, and transfer function identification.
result A framework for effectively dealing with multi-input multi-output robotic data.
The densification of small-cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small-cell base stations. This involves …
A new Q-learning controller improves line follower robot control.
problem Challenges in controlling line follower robots due to unknown mechanical characteristics and uncertainties.
method Simulated annealing based Q learning method to address controller performance issues.
result The proposed controller outperforms conventional P controllers in line follower robots.
Neuro-robotics model shows gestures improve counting ability.
problem Improving counting ability in robots and children.
method Introduces a neuro-robotics model trained with pointing data, tests various model and training conditions.
result Model's performance aligns with human children's learning to count.
Paper addresses hypothesis space misspecification in learning from human demonstrations and corrections.
problem Hypothesis space misspecification in learning from human demonstrations and corrections.
method Reason explicitly about how well the robot can explain human inputs given its hypothesis space.
result Demonstrates method on a 7 DOF robot manipulator.
For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a challenging task. In this paper, we propose a Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like interaction skills thr…
Robot learns agile leg movements from simulations to real robots.
problem Training legged robots for dynamic maneuvers is challenging and expensive.
method Simulation-based reinforcement learning for real legged systems.
result ANYmal robot can follow high-level commands, run faster, and recover from falls.
The paper learns robot skills from demonstrations without supervision.
problem Discovering robotic options from unlabelled demonstrations.
method Temporal variational inference for latent variable learning.
result The framework can learn options across multiple datasets.
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
problem Challenges in working with real robotic hardware, especially position-controlled robots.
method Combines RL with traditional force control techniques, implementing parallel position/force control and admittance control.
result Validated methods on both simulation and real robot (UR3 e-series) for force control.
Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take significant time. In this paper, we focus on learning a realistic world model capturi…
Study aims to develop a humanoid robot dialogue system.
problem Current dialogue systems lack attention to non-verbal cues.
method Participated in a competition to develop a system with facial expressions and gaze control.
result Developed a humanoid robot dialogue system.
Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the robot's hypothesis space. When this is not true, even methods that keep track of uncertainty over the objective fail because they reason about …
Top 8 robotic vision systems tackled lifelong object recognition challenges.
problem Lifelong learning in robotic vision for varied, dynamic environments.
method Design of a dataset with diverse conditions and rules for evaluation.
result Robotic vision systems improved over time with dynamic object appearances.
Paper proposes a method to control robots of different shapes efficiently.
problem Learning optimal control policies for robots of various shapes is challenging.
method Hierarchical architecture with hypernetworks and fixed attention mechanism.
result Method improves learning performance and generalizes to unseen morphologies.
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social intera…
Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the …