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Vision-Based High-Speed Driving With a deep Dynamic Observer

IEEE robotics AND automation LETTERS 2019年第2期4卷 1564-1571页

作者： Drews, Paul Williams, Grady Goldfain, Brian Theodorou, Evangelos A. Rehg, James M. Georgia Inst Technol IRIM Atlanta GA 30332 USA

In this letter, we present a framework for combining deep learning-based road detection, particle filters, and model predictive control (MPC) to drive aggressively using only amonocular camera, IMU, and wheel speed sensors. This framework uses deep convolutional neural networks combined with LSTMs to learn a local cost map representation of the track in front of the vehicle. A particle filter uses this dynamic observation model to localize in a schematic map, and MPC is used to drive aggressively using this particle filter based state estimate. We show extensive real world testing results and demonstrate reliable operation of the vehicle at the friction limits on a complex dirt track. We reach speeds above 27 m/h (12 m/s) on a dirt track with a 105 ft (32 m) long straight using our 1: 5 scale test vehicle.

关键词： deep learning in robotics and automation autonomous vehicle navigation localization computer vision for transportation

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learning Cross Dimension Scene Representation for Interactive Navigation Agents in Obstacle-Cluttered Environments

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IEEE robotics AND automation LETTERS 2024年第7期9卷 6264-6271页

作者： Sang, Hongrui Jiang, Rong Li, Xin Wang, Zhipeng Zhou, Yanmin He, Bin Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201203 Peoples R China

Embodied visual navigation has witnessed significant advancements. However, most studies commonly assume that environments are static and contain at least one collision-free path. In human environments, agents frequently encounter challenges when navigating through scenes with disarranged objects. In this letter, we explore the interactive navigation problem, wherein agents possess the ability to physically interact with and modify the environment, such as moving obstacles aside, to improve their efficiency in reaching the target. To this end, we propose a novel cross dimension scene representation module under the framework of reinforcement learning (RL) that provides joint 2D and 3D scene representation for interactive agents. We first leverage 2D and 3D observation encoders to extract informative features from observations. Subsequently, a joint representation network is proposed to lift the dimension of 2D feature maps to 3D and align them with 3D observation, enabling us to fuse information from different dimensions. This allows us to simultaneously harness the advantages of 2D and 3D observations, thereby yielding a more informative representation for interactive RL agents in addressing challenges arising from physical interactions. We validate our proposed approach in the iGibson environment, and experimental results demonstrate a significant improvement over baseline methods.

关键词： Navigation Three-dimensional displays Visualization Task analysis Robots Semantics Feature extraction Vision-based navigation deep learning in robotics and automation mobile manipulation

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Motion-Based Object Segmentation Based on Dense RGB-D Scene Flow

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3797-3804页

作者： Shao, Lin Shah, Parth Dwaracherla, Vikranth Bohg, Jeannette Stanford Univ Stanford CA 94305 USA

Given two consecutive RGB-D images, we propose a model that estimates a dense three-dimensional (3D) motion field, also known as scene flow. We take advantage of the fact that in robot manipulation scenarios, scenes often consist of a set of rigidly moving objects. Our model jointly estimates the following: First, the segmentation of the scene into an unknown but finite number of objects, second, the motion trajectories of these objects, and finally, the object scene flow. We employ an hourglass, deep neural network architecture. In the encoding stage, the RGB and depth images undergo spatial compression and correlation. In the decoding stage, the model outputs three images containing a per-pixel estimate of the corresponding object center as well as object translation and rotation. This forms the basis for inferring the object segmentation and final object scene flow. To evaluate our model, we generated a new and challenging, large scale, synthetic dataset that is specifically targeted at robotic manipulation: It contains a large number of scenes with a very diverse set of simultaneously moving 3D objects and is recorded with a simulated, static RGB-D camera. In quantitative experiments, we show that we outperform state-of-the-art scene flow and motion-segmentation methods on this data set. In qualitative experiments, we show how our learned model transfers to challenging real-world scenes, visually generating better results than existing methods.

关键词： RGB-D perception object detection segmentation and categorization deep learning in robotics and automation

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LS-VO: learning Dense Optical Subspace for Robust Visual Odometry Estimation

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IEEE robotics AND automation LETTERS 2018年第3期3卷 1735-1742页

作者： Costante, Gabriele Ciarfuglia, Thomas Alessandro Univ Perugia Dept Engn I-06125 Perugia Italy

This work proposes a novel deep network architecture to solve the camera ego-motion estimation problem. A motion estimation network generally learns features similar to optical flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an autoencoder network to find a nonlinear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture latent space visual odometry (LS-VO). The experiments show that LS-VO achieves a considerable increase in performances with respect to baselines, while the number of parameters of the estimation network only slightly increases.

关键词： Computer vision for transportation deep learning in robotics and automation visual learning visual-based navigation

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A Single-Stream Segmentation and Depth Prediction CNN for Autonomous Driving

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IEEE INTELLIGENT SYSTEMS 2021年第4期36卷 79-85页

作者： Aladem, Mohamed Rawashdeh, Samir A. Univ Michigan Dearborn Dearborn MI 48128 USA

Convolutional neural networks (CNN) have been used successfully in solving many challenging visual perception tasks facing mobile robots and self-driving cars. To facilitate deploying such models on embedded hardware onboard mobile robots that have limited resources, multitask learning approaches have become common. In typically used multitask learning, a shared encoder network extracts features from inputs whereas multiple task-specific decoders transform these features into their target output. However, properly combining different tasks' losses into the final network loss such that each task is making progress learning is a major challenge in these approaches. In this article, we present an innovative approach to extend a typical single-task network with the capability of performing two tasks without multiple decoders, i.e., a single-stream two-task network. The two output tasks are semantic segmentation and monocular depth prediction which are essential tasks in visual perception for autonomous driving. The method is centered on solving semantic segmentation with a regression loss function rather than a classification one. With our approach, we seize multitask learning benefits of reduced overhead and enhanced generalization while alleviating the need to balance different loss functions. Experimental evaluations with baseline single tasks and a multitask network are presented.

关键词： Autonomous vehicles Computer Vision for Transportation deep learning in robotics and automation Semantic Scene Understanding

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A Two-Stage Reinforcement learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3098-3105页

作者： Wang, Dawei Fan, Tingxiang Han, Tao Pan, Jia Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China

Unlike autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs) have a higher dimensional configuration space, which makes the motion planning of multi-UAVs a challenging task. In addition, uncertainties and noises are more significant in UAV scenarios, which increases the difficulty of autonomous navigation for multi-UAV. In this letter, we proposed a two-stage reinforcement learning (RL) based multi-UAV collision avoidance approach without explicitly modeling the uncertainty and noise in the environment. Our goal is to train a policy to plan a collision-free trajectory by leveraging local noisy observations. However, the reinforcement learned collision avoidance policies usually suffer from high variance and low reproducibility, because unlike supervised learning, RL does not have a fixed training set with ground-truth labels. To address these issues, we introduced a two-stage training method for RL based collision avoidance. For the first stage, we optimize the policy using a supervised training method with a loss function that encourages the agent to follow the well-known reciprocal collision avoidance strategy. For the second stage, we use policy gradient to refine the policy. We validate our policy in a variety of simulated scenarios, and the extensive numerical simulations demonstrate that our policy can generate time-efficient and collision-free paths under imperfect sensing, and can well handle noisy local observations with unknown noise levels.

关键词： Collision avoidance deep learning in robotics and automation

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DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor With Application to In-Hand Manipulation

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IEEE robotics AND automation LETTERS 2020年第3期5卷 3838-3845页

作者： Lambeta, Mike Chou, Po-Wei Tian, Stephen Yang, Brian Maloon, Benjamin Most, Victoria Rose Stroud, Dave Santos, Raymond Byagowi, Ahmad Kammerer, Gregg Jayaraman, Dinesh Calandra, Roberto Facebook Menlo Pk CA 94025 USA

Despite decades of research, general purpose in-hand manipulation remains one of the unsolved challenges of robotics. One of the contributing factors that limit current robotic manipulation systems is the difficulty of precisely sensing contact forces - sensing and reasoning about contact forces are crucial to accurately control interactions with the environment. As a step towards enabling better robotic manipulation, we introduce DIGIT, an inexpensive, compact, and high-resolution tactile sensor geared towards in-hand manipulation. DIGIT improves upon past vision-based tactile sensors by miniaturizing the form factor to be mountable on multi-fingered hands, and by providing several design improvements that result in an easier, more repeatable manufacturing process, and enhanced reliability. We demonstrate the capabilities of the DIGIT sensor by training deep neural network model-based controllers to manipulate glass marbles in-hand with a multi-finger robotic hand. To provide the robotic community access to reliable and low-cost tactile sensors, we open-source the DIGIT design at ***.

关键词： Tactile sensors Cameras Task analysis Reliability Perception for grasping and manipulation force and tactile sensing deep learning in robotics and automation learning and adaptive systems

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Simultaneous Policy and Discrete Communication learning for Multi-Agent Cooperation

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2498-2505页

作者： Freed, Benjamin Sartoretti, Guillaume Choset, Howie Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore

Decentralized multi-agent reinforcement learning has been demonstrated to be an effective solution to large multi-agent control problems. However, agents typically can only make decisions based on local information, resulting in suboptimal performance in partially-observable settings. The addition of a communication channel overcomes this limitation by allowing agents to exchange information. Existing approaches, however, have required agent output size to scale exponentially with the number of message bits, and have been slow to converge to satisfactory policies due to the added difficulty of learning message selection. We propose an independent bitwise message policy parameterization that allows agent output size to scale linearly with information content. Additionally, we leverage aspects of the environment structure to derive a novel policy gradient estimator that is both unbiased and has a lower variance message gradient contribution than typical policy gradient estimators. We evaluate the impact of these two contributions on a collaborative multi-agent robot navigation problem, in which information must be exchanged among agents. We find that both significantly improve sample efficiency and result in improved final policies, and demonstrate the applicability of these techniques by deploying the learned policies on physical robots.

关键词： Multi-robot systems deep learning in robotics and automation distributed robot systems

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SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching

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IEEE robotics AND automation LETTERS 2021年第4期6卷 8005-8012页

作者： Du, Hongzhi Li, Yanyan Sun, Yanbiao Zhu, Jigui Tombari, Federico Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Tianjin 300072 Peoples R China Tech Univ Munich Dept Informat D-80333 Munich Germany Google Inc Mountain View CA USA

The cost aggregation strategy shows a crucialrole in learning-based stereo matching tasks, where 3D convolutional filters obtain state of the art but require intensive computation resources, while 2D operations need less GPU memory but are sensitive to domain shift. In this letter, we decouple the 4D cubic cost volume used by 3D convolutional filters into sequential cost maps along the direction of disparity instead of dealing with it at once by exploiting a recurrent cost aggregation strategy. Furthermore, a novel recurrent module, Stacked Recurrent Hourglass (SRH), is proposed to process each cost map. Our hourglass network is constructed based on Gated Recurrent Units (GRUs) and down/upsampling layers, which provides GRUs larger receptive fields. Then two hourglass networks are stacked together, while multi-scale information is processed by skip connections to enhance the performance of the pipeline in textureless areas. The proposed architecture is implemented in an end-to-end pipeline and evaluated on public datasets, which reduces GPU memory consumption by up to 56.1% compared with PSMNet using stacked hourglass 3D CNNs without the degradation of accuracy. Then, we further demonstrate the scalability of the proposed method on several high-resolution pairs, while previously learned approaches often fail due to the memory constraint. The code is released at https://***/hongzhidu/SRHNet.

关键词： Computer vision for automation deep learning in robotics and automation

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Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3-D Pedestrian Pose and Gait Prediction

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1501-1508页

作者： Du, Xiaoxiao Vasudevan, Ram Johnson-Roberson, Matthew Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

In applications, such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This letter proposes a biomechanically inspired recurrent neural network that can predict the location and three-dimensional (3-D) articulated body pose of pedestrians in a global coordinate frame, given 3-D poses and locations estimated in prior frames with inaccuracy. The proposed network is able to predict poses and global locations for multiple pedestrians simultaneously, for pedestrians up to 45 m from the cameras (urban intersection scale). The outputs of the proposed network are full-body 3-D meshes represented in skinned multiperson linear model parameters. The proposed approach relies on a novel objective function that incorporates the periodicity of human walking (gait), the mirror symmetry of the human body, and the change of ground reaction forces in a human gait cycle. This letter presents prediction results on the PedX dataset, a large-scale, in-the-wild data set collected at real urban intersections with heavy pedestrian traffic. Results showthat the proposed network can successfully learn the characteristics of pedestrian gait and produce accurate and consistent 3-D pose predictions.

关键词： deep learning in robotics and automation gesture posture and facial expressions kinematics long short-term memory (LSTM) pedestrian gait prediction

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