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Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments

IEEE robotics AND automation LETTERS 2020年第2期5卷 713-720页

作者： Xia, Fei Shen, William B. Li, Chengshu Kasimbeg, Priya Tchapmi, Micael Edmond Toshev, Alexander Martin-Martin, Roberto Savarese, Silvio Stanford Univ SAIL Stanford CA 94305 USA Google Robot Mountain View CA 94043 USA

We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot navigation problems where physical interaction with objects (e.g., pushing) is allowed and even encouraged to reach the goal. Our benchmark comprises two novel elements: 1) a new experimental simulated environment, the Interactive Gibson Environment, that generate photo-realistic images of indoor scenes and simulates realistic physical interactions of robots and common objects found in these scenes;2) the Interactive Navigation Score, a novel metric to study the interplay between navigation and physical interaction of Interactive Navigation solutions. We present and evaluate multiple learning-based baselines in Interactive Gibson Benchmark, and provide insights into regimes of navigation with different trade-offs between navigation, path efficiency and disturbance of surrounding objects. We make our benchmark publicly available(1) and encourage researchers from related robotics disciplines (e.g., planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson Benchmark.

关键词： Visual-based navigation deep learning in robotics and automation mobile manipulation

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deepTIO: A deep Thermal-Inertial Odometry With Visual Hallucination

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1672-1679页

作者： Saputra, Muhamad Risqi U. de Gusmao, Pedro P. B. Lu, Chris Xiaoxuan Almalioglu, Yasin Rosa, Stefano Chen, Changhao Wahlstrom, Johan Wang, Wei Markham, Andrew Trigoni, Niki Univ Oxford Dept Comp Sci Oxford OX1 3QD England

Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a deep Neural Network model for thermal-inertial odometry (deepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model.

关键词： Localization sensor fusion deep learning in robotics and automation thermal-inertial odometry

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Scene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2816-2823页

作者： Ridel, Daniela Deo, Nachiket Wolf, Denis Trivedi, Mohan Univ Sao Paulo Mobile Robot Lab BR-13566 Sao Carlos SP Brazil Univ Calif San Diego Lab Intelligent & Safe Automobiles San Diego CA 92092 USA

Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories. The underlying scene and past motion of agents can provide useful cues to predict their future motion. However, the heterogeneity of the two inputs poses a challenge for learning a joint representation of the scene and past trajectories. To address this challenge, we propose a model based on grid representations to forecast agent trajectories. We represent the past trajectories of agents using binary 2-D grids, and the underlying scene as a RGB birds-eye view (BEV) image, with an agent-centric frame of reference. We encode the scene and past trajectories using convolutional layers and generate trajectory forecasts using a Convolutional LSTM (ConvLSTM) decoder. Results on the publicly available Stanford Drone Dataset (SDD) show that our model outperforms prior approaches and outputs realistic future trajectories that comply with scene structure and past motion.

关键词： Multimodal trajectory forecasting motion and path planning deep learning in robotics and automation

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Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via deep learning

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3299-3306页

作者： Truby, Ryan L. Della Santina, Cosimo Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot's 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot's configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics.

关键词： Modeling control and learning for soft robots soft sensors and actuators deep learning in robotics and automation

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learning Fast Adaptation With Meta Strategy Optimization

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2950-2957页

作者： Yu, Wenhao Tan, Jie Bai, Yunfei Coumans, Erwin Ha, Sehoon Google Robot Mountain View CA 94043 USA Georgia Inst Technol Atlanta GA 30332 USA

The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training policies with latent variable inputs that can quickly adapt to new scenarios with a handful of trials in the target environment. The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases. This allows MSO to effectively learn locomotion skills as well as a latent space that is suitable for fast adaptation. We evaluate our method on a real quadruped robot and demonstrate successful adaptation in various scenarios, including sim-to-real transfer, walking with a weakened motor, or climbing up a slope. Furthermore, we quantitatively analyze the generalization capability of the trained policy in simulated environments. Both real and simulated experiments show that our method outperforms previous methods in adaptation to novel tasks.

关键词： Optimization Training Legged locomotion Adaptation models Physics Task analysis deep learning in robotics and automation learning and adaptive systems legged robots

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Real Time Trajectory Prediction Using deep Conditional Generative Models

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IEEE robotics AND automation LETTERS 2020年第2期5卷 970-976页

作者： Gomez-Gonzalez, Sebastian Prokudin, Sergey Schoelkopf, Bernhard Peters, Jan Max Planck Intelligent Syst D-72072 Tubingen Germany Tech Univ Darmstadt D-64289 Darmstadt Germany

Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it is still challenging to make long term accurate predictions with the low latency required by real time robotic systems. In this letter, we propose a deep conditional generative model for trajectory prediction that is learned from a data set of collected trajectories. Our method uses encoder and decoder deep networks that map complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations. The encoder and decoder networks are trained using stochastic gradient variational Bayes. In the experiments, we show that our model provides more accurate long term predictions with a lower latency than popular models for trajectory forecasting like recurrent neural networks or physical models based on differential equations. Finally, we test our proposed approach in a robot table tennis scenario to evaluate the performance of the proposed method in a robotic task with hard real time constraints.

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

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Open-Loop Position Control in Collaborative, Modular Variable-Stiffness-Link (VSL) Robots

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1772-1779页

作者： Gandarias, Juan M. Wang, Yongjing Stilli, Agostino Garcia-Cerezo, Alfonso J. Gomez-de-Gabriel, Jesus M. Wurdemann, Helge A. Univ Malaga Syst Engn & Automat Dept Malaga 29016 Spain UCL Dept Comp Sci London WC1E 6BT England UCL Dept Mech Engn London WC1E 6BT England

Collaborative robots open up new avenues in the field of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation with humans. The integration and control of variable stiffness elements allow inherently safe interaction: Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collision. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions. In this letter, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during task-oriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links.

关键词： Modeling control and learning for soft robots soft robot materials and design deep learning in robotics and automation

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Generative Localization With Uncertainty Estimation Through Video-CT Data for Bronchoscopic Biopsy

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IEEE robotics AND automation LETTERS 2020年第1期5卷 258-265页

作者： Zhao, Cheng Shen, Mali Sun, Li Yang, Guang-Zhong Imperial Coll London Hamlyn Ctr London SW7 2AZ England Univ Oxford Oxford Robot Inst Oxford OX1 2JD England Shanghai Jiao Tong Univ Inst Med Shanghai 200240 Peoples R China

Robot-assisted endobronchial intervention requires accurate localization based on both intra- and pre-operative data. Most existing methods achieve this by registering 2D videos with 3D CT models according to a defined similarity metric with local features. Instead, we formulate the bronchoscopic localization as a learning-based global localisation using deep neural networks. The proposed network consists of two generative architectures and one auxiliary learning component. The cycle generative architecture bridges the domain variance between the real bronchoscopic videos and virtual views derived from pre-operative CT data so that the proposed approach can be trained through a large number of generated virtual images but deployed through real images. The auxiliary learning architecture leverages complementary relative pose regression to constrain the search space, ensuring consistent global pose predictions. Most importantly, the uncertainty of each global pose is obtained through variational inference by sampling within the learned underlying probability distribution. Detailed validation results demonstrate the localization accuracy with reasonable uncertainty achieved and its potential clinical value. A demonstration video demo can be found on the website https://***/ci9LMY49aF8.

关键词： Computer vision for medical robotics medical robots and systems localization visual learning deep learning in robotics and automation

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Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2451-2458页

作者： Lin, Yu-Chi Righetti, Ludovic Berenson, Dmitry Univ Michigan Ann Arbor MI 48109 USA Max Planck Inst Intelligent Syst D-70569 Tubingen Germany NYU New York NY USA

Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this letter, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling as a result of a disturbance. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.

关键词： Motion and path planning humanoid and bipedal locomotion deep learning in robotics and automation

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learning to Optimally Segment Point Clouds

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IEEE robotics AND automation LETTERS 2020年第2期5卷 875-882页

作者： Hu, Peiyun Held, David Ramanan, Deva Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Argo AI Pittsburgh PA 15213 USA

We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it searches over a set of candidate segmentations and returns one where individual segments score well according to a data-driven point-based model of "objectness". We prove that if we score a segmentation by the worst objectness among its individual segments, there is an efficient algorithm that finds the optimal worst-case segmentation among an exponentially large number of candidate segmentations. We also present an efficient algorithm for the average-case. For evaluation, we repurpose KITTI 3D detection as a segmentation benchmark and empirically demonstrate that our algorithms significantly outperform past bottom-up segmentation approaches and top-down object-based algorithms on segmenting point clouds.

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

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