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learning Natural Locomotion Behaviors for Humanoid Robots Using Human Bias

IEEE robotics AND automation LETTERS 2020年第2期5卷 2610-2617页

作者： Yang, Chuanyu Yuan, Kai Heng, Shuai Komura, Taku Li, Zhibin Univ Edinburgh Edinburgh EH8 9JS Midlothian Scotland Harbin Inst Technol Harbin 150001 Peoples R China

This letter presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concepts from conventional humanoid control. We rigorously validated and benchmarked the learning framework which consistently produced robust locomotion behaviors in various test scenarios. Further, we demonstrated the capability of learning robust and versatile policies in the presence of disturbances, such as terrain irregularities and external pushes.

关键词： deep learning in robotics and automation humanoid and bipedal locomotion learning from demonstration

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Real-Time Soft Body 3D Proprioception via deep Vision-Based Sensing

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3382-3389页

作者： Wang, Ruoyu Wang, Shiheng Du, Songyu Xiao, Erdong Yuan, Wenzhen Feng, Chen NYU Tandon Sch Engn Brooklyn NY 11201 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymax-shaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation (<= 2.5 ms/frame) for robust shape estimation with high Precision (<= 1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing. Our code is available at: https://***/deepSoRo.

关键词： Modeling control and learning for soft robots deep learning in robotics and automation 3D deep learning

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A General Framework for Uncertainty Estimation in deep learning

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3153-3160页

作者： Loquercio, Antonio Segu, Mattia Scaramuzza, Davide Univ Zurich Dept Informat & Neuroinformat CH-8092 Zurich Switzerland Swiss Fed Inst Technol CH-8092 Zurich Switzerland

Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task;(ii) it does not require changes in the optimization process;(iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy. The video available at https://***/X7n-bRS5vSM shows qualitative results of our experiments. The project's code is available at: https://***/s3nygw7.

关键词： deep learning in robotics and automation probability and statistical methods AI-based methods

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Depth Based Semantic Scene Completion With Position Importance Aware Loss

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IEEE robotics AND automation LETTERS 2020年第1期5卷 219-226页

作者： Li, Jie Liu, Yu Yuan, Xia Zhao, Chunxia Siegwart, Roland Reid, Ian Cadena, Cesar Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland

Semantic scene completion (SSC) refers to the task of inferring the 3D semantic segmentation of a scene while simultaneously completing the 3D shapes. We propose PALNet, a novel hybrid network for SSC based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from multi-stages using fine-grained depth information to efficiently capture the context, as well as the geometric cues of the scene. Current methods for SSC treat all parts of the scene equally causing unnecessary attention to the interior of objects. To address this problem, we propose Position Aware Loss (PA-Loss) which is position importance aware while training the network. Specifically, PA-Loss considers Local Geometric Anisotropy to determine the importance of different positions within the scene. It is beneficial for recovering key details like the boundaries of objects and the corners of the scene. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method and its superior performance. Code and demo Video demo can be found here: https://***/j-LAMcMh0yg. are avaliable at https://***/UniLauX/PALNet.

关键词： Semantic scene understanding deep learning in robotics and automation RGB-D perception

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When Your Robot Breaks: Active learning During Plant Failure

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IEEE robotics AND automation LETTERS 2020年第2期5卷 438-445页

作者： Schrum, Mariah L. Gombolay, Matthew C. Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals. To address this challenging problem, we propose probabilistically-safe, online learning techniques to infer the altered dynamics of a robot at the moment a failure (e.g., physical damage) occurs. We combine model predictive control and active learning within a chance-constrained optimization framework to safely and efficiently learn the new plant model of the robot. We leverage a neural network for function approximation in learning the latent dynamics of the robot under failure conditions. Our framework generalizes to various damage conditions while being computationally light-weight to advance real-time deployment. We empirically validate within a virtual environment that we can regain control of a severely damaged aircraft in seconds and require only 0.1 seconds to find safe, information-rich trajectories, outperforming state-of-the-art approaches.

关键词： Aerial systems mechanics and control autonomous agents deep learning in robotics and automation

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Self-Supervised learning of State Estimation for Manipulating Deformable Linear Objects

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2372-2379页

作者： Yan, Mengyuan Zhu, Yilin Jin, Ning Bohg, Jeannette Stanford Univ Sch Engn Stanford CA 94305 USA Stanford Univ Stanford CA 94305 USA Calico Labs San Francisco CA USA

We demonstrate model-based, visual robot manipulation of deformable linear objects. Our approach is based on a state-space representation of the physical system that the robot aims to control. This choice has multiple advantages, including the ease of incorporating physics priors in the dynamics model and perception model, and the ease of planning manipulation actions. In addition, physical states can naturally represent object instances of different appearances. Therefore, dynamics in the state space can be learned in one setting and directly used in other visually different settings. This is in contrast to dynamics learned in pixel space or latent space, where generalization to visual differences are not guaranteed. Challenges in taking the state-space approach are the estimation of the high-dimensional state of a deformable object from raw images, where annotations are very expensive on real data, and finding a dynamics model that is both accurate, generalizable, and efficient to compute. We are the first to demonstrate self-supervised training of rope state estimation on real images, without requiring expensive annotations. This is achieved by our novel self-supervising learning objective, which is generalizable across a wide range of visual appearances. With estimated rope states, we train a fast and differentiable neural network dynamics model that encodes the physics of mass-spring systems. Our method has a higher accuracy in predicting future states compared to models that do not involve explicit state estimation and do not use any physics prior, while only using 3% of training data. We also show that our approach achieves more efficient manipulation, both in simulation and on a real robot, when used within a model predictive controller.

关键词： Visual learning deep learning in robotics and automation perception for grasping and manipulation

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SilhoNet-Fisheye: Adaptation of A ROI Based Object Pose Estimation Network to Monocular Fisheye Images

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IEEE robotics AND automation LETTERS 2020年第3期5卷 4241-4248页

作者： Billings, Gideon Johnson-Roberson, Matthew Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

There has been much recent interest in deep learning methods for monocular image based object pose estimation. While object pose estimation is an important problem for autonomous robot interaction with the physical world, and the application space for monocular-based methods is expansive, there has been little work on applying these methods with fisheye imaging systems. Also, little exists in the way of annotated fisheye image datasets on which these methods can be developed and tested. The research landscape is even more sparse for object detection methods applied in the underwater domain, fisheye image based or otherwise. In this work, we present a novel framework for adapting a ROI-based 6D object pose estimation method to work on full fisheye images. The method incorporates the gnomic projection of regions of interest from an intermediate spherical image representation to correct for the fisheye distortions. Further, we contribute a fisheye image dataset, called UWHandles, collected in natural underwater environments, with 6D object pose and 2D bounding box annotations.

关键词： Computer vision for automation computer vision for other robotic applications deep learning in robotics and automation recognition visual learning

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Data-Efficient and Safe learning for Humanoid Locomotion Aided by a Dynamic Balancing Model

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IEEE robotics AND automation LETTERS 2020年第3期5卷 4376-4383页

作者： Ahn, Junhyeok Lee, Jaemin Sentis, Luis Univ Texas Austin Dept Mech Engn Austin TX 78712 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relied on a low-dimensional robot model, commonly used in high-level Walking Pattern Generators (WPGs). However, a low-level feedback controller cannot precisely track desired footstep locations due to the discrepancies between the full order model and the simplified model. In this study, we propose mitigating this problem by complementing a WPG with reinforcement learning. More specifically, we propose a structured footstep control method consisting of a WPG, a neural network, and a safety controller. The WPG provides an analytical method that promotes efficient learning while the neural network maximizes long-term rewards, and the safety controller encourages safe exploration based on step capturability and the use of control-barrier functions. Our contributions include the following (1) a structured learning control method for locomotion, (2) a data-efficient and safe learning process to improve walking using a physics-based model, and (3) the scalability of the procedure to various types of humanoid robots and walking.

关键词： Humanoid and bipedal locomotion deep learning in robotics and automation model learning for control

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Fast Underwater Image Enhancement for Improved Visual Perception

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3227-3234页

作者： Islam, Md Jahidul Xia, Youya Sattar, Junaed Univ Minnesota Dept Comp Sci & Engn Interact Robot & Vis Lab Minneapolis MN 55455 USA

In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and an unpaired collection of underwater images (of 'poor' and 'good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://***/xahidbuffon/funie-gan.

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

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Federated Imitation learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3509-3516页

作者： Liu, Boyi Wang, Lujia Liu, Ming Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Cloud Comp Lab Shenzhen 518000 Peoples R China Univ Chinese Acad Sci Shenzhen 518000 Peoples R China Hong Kong Univ Sci & Technol Dept ECE Hong Kong Peoples R China Univ Macau Macau 999078 Peoples R China

Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.

关键词： Cloud computing Robot sensing systems Task analysis Fuses Microstrip Computational modeling Big data in robotics and automation deep learning in robotics and automation motion and path planning

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