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Sliding-Window Temporal Attention Based deep learning System for Robust Sensor Modality Fusion for UGV Navigation

IEEE robotics AND automation LETTERS 2019年第4期4卷 4216-4223页

作者： Unlu, Halil Utku Patel, Naman Krishnamurthy, Prashanth Khorrami, Farshad NYU Tandon Sch Engn Dept Elect & Comp Engn Controls Robot Res Lab Brooklyn NY 11201 USA

We propose a novel temporal attention based neural network architecture for robotics tasks that involve fusion of time series of sensor data, and evaluate the performance improvements in the context of autonomous navigation of unmanned ground vehicles (UGVs) in uncertain environments. The architecture generates feature vectors by fusing raw pixel and depth values collected by camera(s) and LiDAR(s), stores a history of the generated feature vectors, and incorporates the temporally attended history with current features to predict a steering command. The experimental studies show the robust performance in unknown and cluttered environments. Furthermore, the temporal attention is resilient to noise, bias, blur, and occlusions in the sensor signals. We trained the network on indoor corridor datasets (that will be publicly released) from our UGV. The datasets have LiDAR depth measurements, camera images, and human tele-operation commands.

关键词： Autonomous vehicle navigation sensor fusion deep learning in robotics and automation

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OriNet: Robust 3-D Orientation Estimation With a Single Particular IMU

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IEEE robotics AND automation LETTERS 2020年第2期5卷 399-406页

作者： Esfahani, Mahdi Abolfazli Wang, Han Wu, Keyu Yuan, Shenghai Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Estimating the robot's heading is a crucial requirement in odometry systems which are attempting to estimate the movement trajectory of a robot. Small errors in the orientation estimation result in a significant difference between the estimated and real trajectory, and failure of the odometry system. The odometry problem becomes much more complicated for micro flying robots since they cannot carry massive sensors. In this manner, they should benefit from the small size and low-cost sensors, such as IMU, to solve the odometry problem, and industries always look for such solutions. However, IMU suffers from bias and measurement noise, which makes the problem of position and orientation estimation challenging to be solved by a single IMU. While there are numerous studies on the fusion of IMU with other sensors, this study illustrates the power of the first deep learning framework for estimating the full 3D orientation of the flying robots (as yaw, pitch, and roll in quaternion coordinates) accurately with the presence of a single IMU. A particular IMU should be utilized during the training and testing of the proposed system. Besides, a method based on the Genetic Algorithm is introduced to measure the IMU bias in each execution. The results show that the proposed method improved the flying robots' ability to estimate their orientation displacement by approximately 80% with the presence of a single particular IMU. The proposed approach also outperforms existing solutions that utilize a monocular camera and IMU simultaneously by approximately 30%.

关键词： Localization SLAM deep learning in robotics and automation autonomous vehicle navigation

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Super-High-Purity Seed Sorter Using Low-Latency Image-Recognition Based on deep learning

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3035-3042页

作者： Heo, Young Jin Kim, Se Jin Kim, Dayeon Lee, Keondo Chung, Wan Kyun Pohang Univ Sci & Technol Dept Mech Engn Pohang Si 37673 South Korea

Most commercial optical sorting systems are designed to achieve high throughput, so they use a naive low-latency image processing for object identification. These naive low-latency algorithms have difficulty in accurately identifying objects with various shapes, textures, sizes, and colors, so the purity of sorted objects is degraded. Current deep learning technology enables robust image detection and classification, but its inference latency requires several milliseconds;thus, deep learning cannot be directly applied to such real-time high throughput applications. We therefore developed a super-high purity seed sorting system that uses a low-latency image-recognition based on a deep neural network and removes the seeds of noxious weeds from mixed seed product at high throughput with accuracy. The proposed system partitions the detection task into localization and classification, and applies batch inference only once strategy;it achieved 500-fps throughput image-recognition including detection and tracking. Based on the classified and tracked results, air ejectors expel the unwanted seeds. This proposed system eliminates almost the whole weeds with small loss of desired seeds, and is superior to current commercial optical sorting systems.

关键词： Agricultural automation deep learning in robotics and automation computer vision for automation

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A General Framework to Increase Safety of learning Algorithms for Dynamical Systems Based on Region of Attraction Estimation

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IEEE TRANSACTIONS ON robotics 2020年第5期36卷 1472-1490页

作者： Zhou, Zhehua Oguz, Ozgur S. Leibold, Marion Buss, Martin Tech Univ Munich Chair Automat Control Engn D-80290 Munich Germany

Although the state-of-the-art learning approaches exhibit impressive results for dynamical systems, only a few applications on real physical systems have been presented. One major impediment is that the intermediate policy during the training procedure may result in behaviors that are not only harmful to the system itself but also to the environment. In essence, imposing safety guarantees for learning algorithms is vital for autonomous systems acting in the real world. In this article, we propose a computationally effective and general safe learning framework, specifically for complex dynamical systems. With a proper definition of the safe region, a supervisory control strategy, which switches the actions applied on the system between the learning-based controller and a predefined corrective controller, is given. A simplified system facilitates the estimation of the safe region for the high-dimensional dynamical system. During the learning phase, the belief of the safe region is updated with the actual execution results of the corrective controller, which in turn enables the learning-based controller to have more freedom in choosing its actions. Two examples are given to demonstrate the performance of the proposed framework, one simple inverted pendulum to illustrate the online adaptation method, and one quadcopter control task to show the overall performance.

关键词： Safety Dynamical systems Heuristic algorithms Reinforcement learning Control systems Task analysis Estimation deep learning in robotics and automation learning and adaptive systems robot safety safe reinforcement learning

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"Elbows Out"-Predictive Tracking of Partially Occluded Pose for Robot-Assisted Dressing

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3598-3605页

作者： Chance, Greg Jevtic, Aleksandar Caleb-Solly, Praminda Alenya, Guillem Torras, Carme Dogramadzi, Sanja Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England UPC CSIC Inst Robot & Informat Ind Barcelona 08028 Spain

Robots that can assist in the activities of daily living, such as dressing, may support older adults, addressing the needs of an aging population in the face of a growing shortage of care professionals. Using depth cameras during robot-assisted dressing can lead to occlusions and loss of user tracking, which may result in unsafe trajectory planning or prevent the planning task proceeding altogether. For the dressing task of putting on a jacket, which is addressed in this letter, tracking of the arm is lost when the user's hand enters the jacket, which may lead to unsafe situations for the user and a poor interaction experience. Using motion tracking data, free from occlusions, gathered from a human human interaction study on an assisted dressing task, recurrent neural network models were built to predict the elbow position of a single arm based on other features of the user pose. The best features for predicting the elbow position were explored by using regression trees indicating the hips and shoulder as possible predictors. Engineered features were also created based on observations of real dressing scenarios and their effectiveness explored. Comparison between position and orientation-based datasets was also included in this study. A 12-fold cross-validation was performed for each feature set and repeated 20 times to improve statistical power. Using position-based data, the elbow position could be predicted with a 4.1 cm error but adding engineered features reduced the error to 2.4 cm. Adding orientation information to the data did not improve the accuracy and aggregating univariate response models failed to make significant improvements. The model was evaluated on Kinect data for a robot dressing task and although not without issues, demonstrates potential for this application. Although this has been demonstrated for jacket dressing, the technique could be applied to a number of different situations during occluded tracking.

关键词： Physical human-robot interaction human detection and tracking deep learning in robotics and automation robot safety

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RLBench: The Robot learning Benchmark & learning Environment

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3019-3026页

作者： James, Stephen Ma, Zicong Arrojo, David Rovick Davison, Andrew J. Imperial Coll London Dyson Robot Lab London SW7 2BU England Imperial Coll London UROP London SW7 2BU England

We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in difficulty from simple target reaching and door opening to longer multi-stage tasks, such as opening an oven and placing a tray in it. We provide an array of both proprioceptive observations and visual observations, which include rgb, depth, and segmentation masks from an over-the-shoulder stereo camera and an eye-in-hand monocular camera. Uniquely, each task comes with an infinite supply of demos through the use of motion planners operating on a series of waypoints given during task creation time;enabling an exciting flurry of demonstration-based learning possibilities. RLBench has been designed with scalability in mind;new tasks, along with their motion-planned demos, can be easily created and then verified by a series of tools, allowing users to submit their own tasks to the RLBench task repository. This large-scale benchmark aims to accelerate progress in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning. With the benchmark's breadth of tasks and demonstrations, we propose the first large-scale few-shot challenge in robotics. We hope that the scale and diversity of RLBench offers unparalleled research opportunities in the robot learning community and beyond. Benchmarking code and videos can be found at https://***/view/rlbench.

关键词： Task analysis Benchmark testing Cameras learning (artificial intelligence) Tools Robot vision systems learning from demonstration deep learning in robotics and automation performance evaluation and benchmarking perception for grasping and manipulation

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SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

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IEEE robotics AND automation LETTERS 2020年第2期5卷 612-619页

作者： Chen, Steven W. Nardari, Guilherme, V Lee, Elijah S. Qu, Chao Liu, Xu Romero, Roseli Ap Francelin Kumar, Vijay Univ Penn Grasp Lab Philadelphia PA 19104 USA Univ Sao Paulo Robot Learning Lab ICMC BR-13566590 Sao Carlos SP Brazil

This letter describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

关键词： robotics in agriculture and forestry SLAM deep learning in robotics and automation virtual reality and interfaces

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RL-RRT: Kinodynamic Motion Planning via learning Reachability Estimators From RL Policies

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4298-4305页

作者： Chiang, Hao-Tien Lewis Hsu, Jasmine Fiser, Marek Tapia, Lydia Faust, Aleksandra Google AI Robot Google Mountain View CA 94043 USA Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA

This letter addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference.

关键词： Motion and path planning learning and adaptive systems deep learning in robotics and automation

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On-Policy Dataset Synthesis for learning Robot Grasping Policies Using Fully Convolutional deep Networks

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1357-1364页

作者： Satish, Vishal Mahler, Jeffrey Goldberg, Ken Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94705 USA Univ Calif Berkeley Dept Ind Operat & Engn Res Berkeley CA 94705 USA

Rapid and reliable robot grasping for a diverse set of objects has applications from warehouse automation to home de-cluttering. One promising approach is to learn deep policies from synthetic training datasets of point clouds, grasps, and rewards sampled using analytic models with stochastic noise models for domain randomization. In this letter, we explore how the distribution of synthetic training examples affects the rate and reliability of the learned robot policy. We propose a synthetic data sampling distribution that combines grasps sampled from the policy action set with guiding samples from a robust grasping supervisor that has full state knowledge. We use this to train a robot policy based on a fully convolutional network architecture that evaluates millions of grasp candidates in 4-DOF (3-D position and planar orientation). Physical robot experiments suggest that a policy based on fully convolutional grasp quality CNNs (FC-GQ-CNNs) can plan grasps in 0.625 s, considering 5000x more grasps than our prior policy based on iterative grasp sampling and evaluation. This computational efficiency improves rate and reliability, achieving 296 mean picks per hour (MPPH) compared to 250 MPPH for iterative policies. Sensitivity experiments explore the effect of supervisor guidance level and granularity of the policy action space. Code, datasets, videos, and supplementary material can be found at http://***/fcgqcnn.

关键词： Grasping deep learning in robotics and automation

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Multi-View Incremental Segmentation of 3-D Point Clouds for Mobile Robots

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1240-1246页

作者： Chen, Jingdao Cho, Yong Kwon Kira, Zsolt Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA

Mobile robots need to create high-definition three-dimensional (3-D) maps of the environment for applications such as remote surveillance and infrastructure mapping. Accurate semantic processing of the acquired 3-D point cloud is critical for allowing the robot to obtain a high-level understanding of the surrounding objects and perform context-aware decision making. Existing techniques for point cloud semantic segmentation are mostly applied on a single frame or offline basis, with no way to integrate the segmentation results over time. This letter proposes an online method for mobile robots to incrementally build a semantically rich 3-D point cloud of the environment. The proposed deep neural network, MCPNet, is trained to predict class labels and object instance labels for each point in the scanned point cloud in an incremental fashion. A multi-view context pooling (MCP) operator is used to combine point features obtained from multiple viewpoints to improve the classification accuracy. The proposed architecture was trained and evaluated on ray-traced scans derived from the Stanford 3-D Indoor Spaces dataset. Results show that the proposed approach led to 15% improvement in pointwise accuracy and 7% improvement in normalized mutual information compared to the next best online method, with only a 6% drop in accuracy compared to the PointNet-based offline approach.

关键词： deep learning in robotics and automation object detection segmentation and categorization semantic scene understanding

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