Improved UAV navigation and landing using deep learning.
problem Autonomous navigation and landing of UAVs with high accuracy.
method Multimodal fusion of visual and inertial sensor data using deep neural networks.
result 25% improvement in pose estimation accuracy compared to traditional methods.
Deep learning agent improves pedestrian navigation in urban environments.
problem Autonomous driving among pedestrians in urban areas.
method Multi-objective deep reinforcement learning using a deep Q-learning variant.
result The multi-objective DQN agent outperforms single-objective DQN in various environments.
Capsule Networks improve autonomous navigation in sparse environments.
problem Challenges in reinforcement learning for sparse reward environments.
method Caps-EM pairs CapsNets with Advantage Actor Critic, using fewer parameters.
result Caps-EM achieves significant time improvements and fewer parameters compared to competing methods.
Adversarial fog tests autonomous navigation models.
problem Neural networks are fooled by adversarial perturbations, but fog naturally creates similar perturbations.
method Introduced a new type of adversarial perturbation using generative models and Cycle-Consistent Generative Adversarial Networks.
result Generated adversarial fog images help test autonomous navigation models.
This research introduces an autonomous robot navigation method using reinforcement learning.
problem Improving robot navigation in complex environments.
method Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models for path planning and decision-making.
result The models enhance robot navigation ability and adaptive learning in unknown environments.
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.
The paper teaches robots to navigate by learning costs from expert demonstrations.
problem Teaching robots to navigate autonomously using only expert observations.
method Developed a map encoder and cost encoder to infer semantic class probabilities and a cost function from expert observations.
result Robots can learn to follow traffic rules in a simulator using only semantic observations.
End-to-end driving network learns navigation and localization from raw data.
problem Lack of full action distribution and localization in end-to-end autonomous driving.
method Variational network for predicting control commands and deterministic navigation, probabilistic localization using noisy GPS.
result Model can predict full probability distribution over possible actions and navigate routes.
Study classifies surface types for autonomous indoor robots using inertial data.
problem Classifying surface types for wheeled robots in indoor environments.
method Prepared a time series dataset of inertial measurements, used deep learning and ensemble machine learning models.
result Baseline model achieved over 68% accuracy on a nine-category surface type dataset.
Study presents a low-cost local motion planner for vineyard navigation.
problem Autonomous navigation in vineyards with limited resources.
method RGB-D camera, dual layer control algorithm, deep learning synergy.
result Robust motion planning for vineyard navigation achieved.
AI mirrors modern math's autonomous development, raising interpretive challenges.
problem AI's effectiveness in math mirrors historical autonomy of math.
method Analyzes historical evolution of modern mathematics and AI's role.
result AI's affinity with math's historical autonomy suggests interpretive limits.
Meta-algorithm reduces exploration steps in changing CMPs.
problem Navigating in environments where transition probabilities change abruptly.
method MNM learning meta-algorithm, exploration steps as performance measure.
result Upper bound on exploration steps in terms of changes.
RL policy tracks dynamic targets in partially known environments robustly.
problem Active target tracking in partially known environments.
method Deep reinforcement learning (RL) approach for in-sight tracking, navigation, and exploration.
result Unified RL policy shows robust behavior for agile and anomalous targets.
Deep RL predicts car steering angles from images.
problem Learning steering angles for autonomous cars in simulators.
method Extracts latent representations, trains RL on latent vectors.
result Method learns steering angles without human control signals.
MIDAS learns to adaptively control other cars in urban driving scenarios.
problem Autonomous vehicles need to interact with other agents on the road.
method Reinforcement learning with attention mechanism to handle multiple agents.
result MIDAS policies are adaptive and robust to external changes.
This review covers methods for autonomous driving including tracking, prediction, and decision making.
problem Improving autonomous driving through better tracking, prediction, and decision making.
method Approaches based on neural networks, stochastic techniques, and reinforcement learning are discussed.
result Effective methods for autonomous driving are identified and compared.
Survey examines challenges of ML in avionic systems certification.
problem Challenges in current certification standards for ML in avionic systems.
method Literature review focusing on robustness and explainability of ML results.
result Current certification standards do not support ML in avionic systems.
Paper introduces timing-based adversarial attacks on DRL-based navigation systems.
problem Vulnerability of DRL-based navigation systems to adversarial attacks.
method Timing-based adversarial strategies using physical noise patterns.
result Adversarial timing attacks significantly degrade DRL-based navigation performance.
The paper develops a method to learn navigation costs from expert demonstrations in partially observable environments.
problem Learning navigation costs from expert demonstrations in partially observable environments.
method Develops a cost function representation composed of a probabilistic occupancy encoder and a cost encoder, optimized by differentiating the error between demonstrated controls and a control policy computed from the cost encoder.
result The method outperforms baseline IRL algorithms in robot navigation tasks, improving both training and test-time efficiency.
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planni…
PhysVarMix predicts diverse urban trajectories with physics constraints.
problem Predicting complex urban agent trajectories with multiple plausible scenarios.
method Physics-informed variational mixture model combining learning and physics constraints.
result Superior performance compared to existing methods on benchmark datasets.
Advances in deep learning and reinforcement learning enable autonomous UAS.
problem Current UAS lack autonomy, limiting their applications.
method Discussing deep learning and reinforcement learning techniques for UAS autonomy.
result Machine learning can enhance UAS autonomy and expand their use.
Improved multi-step prediction of drivable space for autonomous vehicles.
problem Accurate prediction of drivable space for safer, more comfortable navigation.
method Recurrent Neural Network (RNN) architectures trained on KITTI dataset, incorporating motion features.
result Significant improvement in prediction accuracy over state-of-the-art methods.
Paper proposes an intersection decision algorithm for autonomous vehicles.
problem Navigating intersections with non-automated vehicles.
method Combines reinforcement learning for high-level decisions and model predictive control for low-level planning.
result The proposed algorithm outperforms another controller in success rate and training episodes.
Proposes a new pedestrian prediction model using urban data.
problem Predicting future pedestrian behavior for safer autonomous vehicle navigation.
method Growing Hidden Markov Model (GHMM) extension using cost maps and Natural Vision principles.
result Predicts pedestrian positions more accurately over a longer horizon.
WebGUM learns web navigation from multimodal data, outperforming previous methods.
problem Limited generalization from domain-specific models in web navigation.
method Instruction-following multimodal agent trained on vision-language foundation models.
result Significant improvement in web navigation performance on benchmarks.
Proposes CTSDG model for better vehicle intention prediction across domains.
problem Domain generalization for vehicle intention prediction in dynamic environments.
method Structural causal model with recurrent latent variable integration.
result Consistent improvement in prediction accuracy compared to state-of-the-art methods.
Paper presents a TL approach to reduce drone training time and energy consumption.
problem Limited power and compute capability of edge drones.
method Transfer Learning for deep neural network training via Deep Reinforcement Learning.
result 3.7x reduction in energy consumption and 1.8x reduction in training latency.
Automated testing framework finds weaknesses in deep control policies.
problem Safety of deep neural network control policies is difficult to validate.
method Adversarial reinforcement learning to test and find weaknesses.
result Framework finds weaknesses not evident during manual testing.
Y-GAN uses multi-camera data to estimate depth maps without expensive hardware.
problem Depth perception for autonomous systems requires accurate 3D spatial information.
method Proposes Y-GAN, a deep convolutional generative adversarial network.
result Y-GAN estimates depth maps from multi-camera stereo images without ground truth data.
Deep learning enables a robot to navigate autonomously within a perimeter.
problem Autonomous path planning for space exploration robots.
method Deep reinforcement learning with randomized reward function parameters.
result Trained robot can navigate to any point within a perimeter without prior knowledge.
CGNS predicts probabilistic trajectories for safer autonomous systems.
problem Accurate probabilistic trajectory prediction for dynamic obstacles in complex scenarios.
method CGNS combines latent space learning and variational divergence minimization, incorporating static and interaction information with soft attention mechanisms and regularization for soft constraints.
result CGNS outperforms baseline approaches in pedestrian trajectory prediction and naturalistic driving datasets.
Model-based approaches bear great promise for decision making of agents interacting with the physical world. In the context of spatial environments, different types of problems such as localisation, mapping, navigation or autonomous exploration are typically adressed with specialised methods, often relying on detailed …
Paper presents a self-supervised method to infer road lane networks.
problem Difficult and costly to create lane maps for autonomous vehicles.
method Self-supervised learning using neural and search-based model.
result Model can generalize to new road layouts, unlike previous approaches.
SafeCritic predicts safe trajectories for pedestrians and cyclists avoiding collisions.
problem Predicting safe trajectories for pedestrians and cyclists in urban environments.
method Generative adversarial networks and reinforcement learning to generate safe trajectories, evaluated by a Discriminator.
result Significant improvement over state-of-the-art models in safety classification.
SANE improves exploration of noisy, multimodal functions by finding multiple optima.
problem Finding multiple optima in noisy, non-differentiable functions.
method Strategic Autonomous Non-Smooth Exploration (SANE) with a cost-driven acquisition function and human knowledge gate.
result SANE outperforms classical Bayesian optimization in discovering multiple optima.
In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundi…
Proposes a method to model multi-vehicle interactions using Gaussian processes.
problem Challenges in modeling correlations between multiple road users over time.
method Uses a stochastic vector field model and non-parametric Bayesian learning.
result Captures motion patterns from complex multi-vehicle interactions without heroic prior assumptions.
CARL controls a quadruped to move naturally in complex environments.
problem Motion synthesis in dynamic environments with complex constraints.
method CARL uses GANs to adapt high-level controls to action distributions and deep reinforcement learning for dynamic recovery.
result CARL can be controlled with high-level directives and react naturally to dynamic environments.
Robots navigate wilderness trails using virtual-to-real-world transfer learning.
problem Autonomous navigation of outdoor trails is challenging due to lack of annotated training data.
method Virtual-to-real-world transfer learning with deep learning models trained on synthetic data.
result Classification accuracies of up to 95% on synthetic data and feasibility in real-world trails.
Deep learning models need accurate uncertainty quantification for safe use.
problem Uncertainty in deep learning models, especially for black box models.
method Model multivariate uncertainty for regression problems using neural networks, incorporating aleatoric and epistemic sources of heteroscedastic uncertainty. Train using direct multivariate Gaussian density loss function and end-to-end Kalman filter training.
result Accurate multivariate uncertainty quantification improves Kalman filter performance for in-domain and out-of-domain evaluation data.
Study on bifurcations in Lagrangian systems and geodesics on manifolds.
problem Investigating bifurcations in Lagrangian systems and geodesics on Finsler and Riemannian manifolds.
method Employing Morse index and nullity techniques, and refining the Gauss lemma.
result Derivation of precise conditions for bifurcations in geodesic flows.
HRL4IN tackles interactive navigation tasks with mobile manipulators, improving efficiency and performance.
problem Interactive Navigation tasks require mobile manipulators to perform various actions, but choosing the right part of the embodiment is inefficient.
method HRL4IN uses a hierarchical reinforcement learning architecture to handle heterogeneous phases of navigation and manipulation, selecting the appropriate part of the embodiment for each phase.
result HRL4IN significantly outperforms flat PPO and HAC in terms of task performance and energy efficiency.
Introduces a natural parallel translation for navigation data.
problem Navigation data geometric representation and parallelism.
method Introduces a natural parallel translation using Riemannian parallelism.
result The natural parallel translation preserves the Randers norm and has a finite-dimensional holonomy group.
Deep RL policies can leak private information from trained policies.
problem Privacy leakage in deep reinforcement learning models.
method Environment dynamics search via genetic algorithm and candidate inference based on shadow policies.
result 95.83% average recovery rate of floor plans from trained Grid World navigation DRL agents.
Challenge to separate Earth's magnetic field from vehicle's magnetic field for accurate navigation.
problem Separate Earth's magnetic field from vehicle's magnetic field for accurate magnetic navigation.
method Use machine learning (ML) and integrate physics of magnetic navigation (SciML) to remove aircraft magnetic field from total magnetic field.
result A model can be constructed to effectively remove aircraft magnetic field from the dataset.
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.
Navigation in Lorentz Finsler geometry induces isoparametric hypersurfaces.
problem Defining and analyzing isoparametric hypersurfaces in Lorentz Finsler geometry.
method Using a navigation process with a Finsler metric and a tangent vector field, isoparametric functions and hypersurfaces are defined and analyzed.
result Local correspondences between isoparametric functions and hypersurfaces are established.