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Uncertainty Estimation for Data-Driven Visual Odometry

IEEE TRANSACTIONS ON robotics 2020年第6期36卷 1738-1757页

作者： Costante, Gabriele Mancini, Michele Univ Perugia Dept Engn I-06125 Perugia Italy

Over the past few years, we have witnessed a considerable diffusion of data-driven visual odometry (VO) approaches as viable alternatives to standard geometric-based strategies. Their success is mainly related to the improved robustness to image nonideal conditions (e.g., blur, high or low contrast, texture-poor scenarios). However, most of the data-driven State-of-the-Art (SotA) approaches do not provide any kind of information about the uncertainty of their estimates, which is crucial to effectively integrate them into robotic navigation systems. Inspired by this considerations, we propose uncertainty-aware VO (UA-VO), a novel deep neural network (DNN) architecture that computes relative pose predictions by processing sequence of images and, at the same time, provides uncertainty measures about those estimations. The confidence measure computed by UA-VO considers both epistemic and aleatoric uncertainties and accounts for heteroscedasticity, i.e., it is sample-dependent. We assess the benefits of UA-VO with different typology of experiments on three publicly available datasets and on a brand new set of sequences, we gathered to extend the evaluation.

关键词： Uncertainty Estimation Measurement uncertainty Computational modeling Training Computer architecture Feature extraction Computer vision for transportation deep learning in robotics and automation localization visual learning

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deep Reinforcement learning for Instruction Following Visual Navigation in 3D Maze-Like Environments

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1175-1182页

作者： Devo, Alessandro Costante, Gabriele Valigi, Paolo Univ Perugia Dept Engn I-06125 Perugia Italy

In this work, we address the problem of visual navigation by following instructions. In this task, the robot must interpret a natural language instruction in order to follow a predefined path in a possibly unknown environment. Despite different approaches have been proposed in the last years, they are all based on the assumption that the environment contains objects or other elements that can be used to formulate instructions, such as houses or offices. On the contrary, we focus on situations where the environment objects cannot be used to specify a navigation path. In particular, we consider 3D maze-like environments as our test bench because they can be very large and offer very intricate structures. We show that without reference points, visual navigation and instruction following can be rather challenging, and that standard approaches can not be applied successfully. For this reason, we propose a new architecture that explicitly learns both visual navigation and instruction understanding. We demonstrate with simulated experiments that our method can effectively follow instructions and navigate in previously unseen mazes of various sizes.

关键词： deep learning in robotics and automation visual-based navigation visual learning

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Path Planning With Local Motion Estimations

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2586-2593页

作者： Guzzi, Jerome Chavez-Garcia, R. Omar Nava, Mirko Gambardella, Luca Maria Giusti, Alessandro USI SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Lugano Switzerland

We introduce a novel approach to long-range path planning that relies on a learned model to predict the outcome of local motions using possibly partial knowledge. The model is trained from a dataset of trajectories acquired in a self-supervised way. Sampling-based path planners use this component to evaluate edges to be added to the planning tree. We illustrate the application of this pipeline with two robots: a complex, simulated, quadruped robot (ANYmal) moving on rough terrains;and a simple, real, differential-drive robot (Mighty Thymio), whose geometry is assumed unknown, moving among obstacles. We quantitatively evaluate the model performance in predicting the outcome of short moves and long-range paths;finally, we show that planning results in reasonable paths.

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

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Ambiguity in Sequential Data: Predicting Uncertain Futures With Recurrent Models

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2935-2942页

作者： Berlati, Alessandro Scheel, Oliver Di Stefano, Luigi Tombari, Federico Univ Bologna Dept Comp Sci & Engn I-40136 Bologna Italy BMW Grp D-81927 Munich Germany Tech Univ Munich Fac Comp Sci D-85748 Garching Germany Google CH-8002 Zurich Switzerland

Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data, which is of special importance, as often multiple futures are equally likely. Our approach can be applied to the most common recurrent architectures and can be used with any loss function. Additionally, we introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties and coincides with our intuitive understanding of correctness in the presence of multiple labels. We test our method on several experiments and across diverse tasks dealing with time series data, such as trajectory forecasting and maneuver prediction, achieving promising results.

关键词： Predictive models Measurement Computer architecture Data models Trajectory Training Task analysis deep learning in robotics and automation autonomous agents intelligent transportation systems

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A deep learning Approach to Grasping the Invisible

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2232-2239页

作者： Yang, Yang Liang, Hengyue Choi, Changhyun Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55414 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55414 USA

We study an emerging problem named "grasping the invisible" in robotic manipulation, in which a robot is tasked to grasp an initially invisible target object via a sequence of pushing and grasping actions. In this problem, pushes are needed to search for the target and rearrange cluttered objects around it to enable effective grasps. We propose to solve the problem by formulating a deep learning approach in a critic-policy format. The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning. We divide the problem into two subtasks, and two policies are proposed to tackle each of them, by combining the critic predictions and relevant domain knowledge. A Bayesian-based policy accounting for past action experience performs pushing to search for the target;once the target is found, a classifier-based policy coordinates target-oriented pushing and grasping to grasp the target in clutter. The motion critic and the classifier are trained in a self-supervised manner through robot-environment interactions. Our system achieves a 93% and 87% task success rate on each of the two subtasks in simulation and an 85% task success rate in real robot experiments on the whole problem, which outperforms several baselines by large margins. Supplementary material is available at https://***/***/grasping-invisible.

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

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Self-Supervised Correspondence in Visuomotor Policy learning

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IEEE robotics AND automation LETTERS 2020年第2期5卷 492-499页

作者： Florence, Peter Manuelli, Lucas Tedrake, Russ MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

In this letter, we explore using self-supervised correspondence for improving the generalization performance and sample efficiency of visuomotor policy learning. Prior work has primarily used approaches such as autoencoding, pose-based losses, and end-to-end policy optimization in order to train the visual portion of visuomotor policies. We instead propose an approach using self-supervised dense visual correspondence training and show that this enables visuomotor policy learning with surprisingly high generalization performance with modest amounts of data. Using imitation learning, we demonstrate extensive hardware validation on challenging manipulation tasks with as few as 50 demonstrations. Our learned policies can generalize across classes of objects, react to deformable object configurations, and manipulate textureless symmetrical objects in a variety of backgrounds, all with closed-loop, real-time vision-based policies. Simulated imitation learning experiments suggest that correspondence training offers sample complexity and generalization benefits compared to autoencoding and end-to-end training.

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

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Exploiting Bias for Cooperative Planning in Multi-Agent Tree Search

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1819-1826页

作者： Ma, Aaron Ouimet, Michael Cortes, Jorge Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA Naval Informat Warfare Ctr Pacific San Diego CA 92152 USA

Graph search over states and actions is a valuable tool for robotic planning and navigation. However, the required computation is sensitive to the size of the state and action spaces, a fact which is further exacerbated in multi-agent planning by the number of agents and the presence of sparse reward signals dependent on the cooperation of agents. To tackle these problems, we introduce an algorithm that is pre-trained in a centralized fashion but implemented on robots in a distributed way at runtime. The centralized portion uses imitation learning to iteratively construct policies that help guide an individual agent's own runtime search as well as predict other agents' future actions by exploiting previously discovered joint actions. Our algorithm includes a novel method of tree search based on a mixture of the individual and joint action space, which can be interpreted as a cascading effect where agents are biased by exploration of new actions, exploitation of previously profitable ones, and recommendation provided by deep neural nets. Simulations show the efficacy of the proposed method in cooperative scenarios with sparse rewards.

关键词： Planning scheduling and coordination deep learning in robotics and automation multi-robot systems

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deepGait: Planning and Control of Quadrupedal Gaits Using deep Reinforcement learning

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3699-3706页

作者： Tsounis, Vassilios Alge, Mitja Lee, Joonho Farshidian, Farbod Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8092 Zurich Switzerland

This letter addresses the problem of legged locomotion in non-flat terrain. As legged robots such as quadrupeds are to be deployed in terrains with geometries which are difficult to model and predict, the need arises to equip them with the capability to generalize well to unforeseen situations. In this work, we propose a novel technique for training neural-network policies for terrain-aware locomotion, which combines state-of-the-art methods for model-based motion planning and reinforcement learning. Our approach is centered on formulating Markov decision processes using the evaluation of dynamic feasibility criteria in place of physical simulation. We thus employ policy-gradient methods to independently train policies which respectively plan and execute foothold and base motions in 3D environments using both proprioceptive and exteroceptive measurements. We apply our method within a challenging suite of simulated terrain scenarios which contain features such as narrow bridges, gaps and stepping-stones, and train policies which succeed in locomoting effectively in all cases.

关键词： Planning Foot Legged locomotion Optimization Machine learning Dynamics Legged robots deep learning in robotics and automation motion and path planning

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Geometrically Mappable Image Features

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2062-2069页

作者： Thoma, Janine Paudel, Danda Pani Chhatkuli, Ajad Gool, Luc Van ETH Comp Vis Lab CH-8092 Zurich Switzerland Katholieke Univ Leuven B-3000 Leuven Belgium

Vision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gaining popularity due to recent advances in machine learning. Features that uniquely describe the visual contents of images have a wide range of applications, including image retrieval and understanding. In this work, we propose a method that learns image features targeted for image-retrieval-based localization. Retrieval-based localization has several benefits, such as easy maintenance and quick computation. However, the state-of-the-art features only provide visual similarity scores which do not explicitly reveal the geometric distance between query and retrieved images. Knowing this distance is highly desirable for accurate localization, especially when the reference images are sparsely distributed in the scene. Therefore, we propose a novel loss function for learning image features which are both visually representative and geometrically relatable. This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional. In our experiments we show that our features not only offer significantly better localization accuracy, but also allow to estimate the trajectory of a query sequence in absence of the reference images.

关键词： Localization deep learning in robotics and automation mapping

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Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement learning from Demonstration

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IEEE TRANSACTIONS ON robotics 2020年第4期36卷 1207-1218页

作者： Keller, Brenton Draelos, Mark Zhou, Kevin Qian, Ruobing Kuo, Anthony N. Konidaris, George Hauser, Kris Izatt, Joseph A. Duke Univ Dept Biomed Engn Durham NC 27708 USA Duke Univ Med Ctr Dept Ophthalmol Durham NC 27708 USA Brown Univ Dept Comp Sci Providence RI 02906 USA Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. In this article, we demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This article shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT-guided robotic ophthalmic surgery.

关键词： Needles Robots Microsurgery Cornea learning (artificial intelligence) Task analysis deep learning in robotics and automation learning from demonstration medical robots and systems microsurgery

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