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  • Swarm robotics and complex behaviour of continuum material
    arXiv.cs.RO Pub Date : 2020-04-01
    R. dell'Erba

    In swarm robotics, just as for an animal swarm in Nature, one of the aims is to reach and maintain a desired configuration. One of the possibilities for the team, to reach this aim, is to see what its neighbours are doing. This approach generates a rules system governing the movement of the single robot just by reference to neighbour's motion. The same approach is used in position based dynamics to

    更新日期:2020-04-08
  • Variable Autonomy of Whole-body Control for Inspection and Intervention in Industrial Environments using Legged Robots
    arXiv.cs.RO Pub Date : 2020-04-06
    Guiyang Xin; Carlo Tiseo; Wouter Wolfslag; Joshua Smith; Oguzhan Cebe; Zhibin Li; Sethu Vijayakumar; Michael Mistry

    The deployment of robots in industrial and civil scenarios is a viable solution to protect operators from danger and hazards. Shared autonomy is paramount to enable remote control of complex systems such as legged robots, allowing the operator to focus on the essential tasks instead of overly detailed execution. To realize this, we proposed a comprehensive control framework for inspection and intervention

    更新日期:2020-04-08
  • Control of Unknown Nonlinear Systems with Linear Time-Varying MPC
    arXiv.cs.RO Pub Date : 2020-04-06
    Dimitris Papadimitriou; Ugo Rosolia; Francesco Borrelli

    We present a Model Predictive Control (MPC) strategy for unknown input-affine nonlinear dynamical systems. A non-parametric method is used to estimate the nonlinear dynamics from observed data. The estimated nonlinear dynamics are then linearized over time varying regions of the state space to construct an Affine Time Varying (ATV) model. Error bounds arising from the estimation and linearization procedure

    更新日期:2020-04-08
  • Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
    arXiv.cs.RO Pub Date : 2020-04-07
    Yeping Hu; Wei Zhan; Masayoshi Tomizuka

    Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. A number of methodologies have been proposed to solve prediction problems

    更新日期:2020-04-08
  • CrowdSteer: Realtime Smooth and Collision-Free Robot Navigation in Dense Crowd Scenarios Trained using High-Fidelity Simulation
    arXiv.cs.RO Pub Date : 2020-04-07
    Jing Liang; Utsav Patel; Adarsh Jagan Sathyamoorthy; Dinesh Manocha

    We present a novel high fidelity 3-D simulator that significantly reduces the sim-to-real gap for collision avoidance in dense crowds using Deep Reinforcement Learning-based (DRL). Our simulator models realistic crowd and pedestrian behaviors, along with friction, sensor noise and delays in the simulated robot model. We also describe a technique to incrementally control the randomness and complexity

    更新日期:2020-04-08
  • Curved patch mapping and tracking for irregular terrain modeling: Application to bipedal robot foot placement
    arXiv.cs.RO Pub Date : 2020-04-05
    Dimitrios Kanoulas; Nikos G. Tsagarakis; Marsette Vona

    Legged robots need to make contact with irregular surfaces, when operating in unstructured natural terrains. Representing and perceiving these areas to reason about potential contact between a robot and its surrounding environment, is still largely an open problem. This paper introduces a new framework to model and map local rough terrain surfaces, for tasks such as bipedal robot foot placement. The

    更新日期:2020-04-08
  • Learning to Accelerate Decomposition for Multi-Directional 3D Printing
    arXiv.cs.RO Pub Date : 2020-03-17
    Chenming Wu; Yong-Jin Liu; Charlie C. L. Wang

    As a strong complementary of additive manufacturing, multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures. Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model. Different printing directions are employed in different regions so that a model can

    更新日期:2020-04-08
  • RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar
    arXiv.cs.RO Pub Date : 2020-04-02
    Prannay Kaul; Daniele De Martini; Matthew Gadd; Paul Newman

    This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using FMCW scanning radar. We advocate radar over the traditional sensors used for this task as it operates at longer ranges and is substantially more robust to adverse weather and illumination conditions. We avoid laborious manual labelling by exploiting

    更新日期:2020-04-08
  • Simultaneous Learning from Human Pose and Object Cues for Real-Time Activity Recognition
    arXiv.cs.RO Pub Date : 2020-03-26
    Brian Reily; Qingzhao Zhu; Christopher Reardon; Hao Zhang

    Real-time human activity recognition plays an essential role in real-world human-centered robotics applications, such as assisted living and human-robot collaboration. Although previous methods based on skeletal data to encode human poses showed promising results on real-time activity recognition, they lacked the capability to consider the context provided by objects within the scene and in use by

    更新日期:2020-04-08
  • Self-propulsion on spandex: toward a robotic analog gravity system
    arXiv.cs.RO Pub Date : 2020-04-07
    Shengkai Li; Yasemin Ozkan Aydin; Gabriella Small; Charles Xiao; Jennifer M. Rieser; Hussain N. Gynai; Pablo Laguna; Daniel I. Goldman

    Numerous laboratory systems have been proposed as analogs to study phenomena (like black holes, Hawking radiation) associated with Einstein's theory of General Relativity (GR) but which are challenging to study in experimental or simulated astrophysical settings. Such analogs, typically acoustic, fluid, or atomic systems require delicate manipulation. Here we introduce a robotic system that captures

    更新日期:2020-04-08
  • Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
    arXiv.cs.RO Pub Date : 2020-04-07
    Jiaming Sun; Linghao Chen; Yiming Xie; Siyu Zhang; Qinhong Jiang; Xiaowei Zhou; Hujun Bao

    In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network

    更新日期:2020-04-08
  • Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics
    arXiv.cs.RO Pub Date : 2016-02-14
    Felix Berkenkamp; Andreas Krause; Angela P. Schoellig

    Robotic algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance. Optimization algorithms, such as Bayesian optimization, have been used to automate this process

    更新日期:2020-04-08
  • Circuminvariants of 3-Periodics in the Elliptic Billiard
    arXiv.cs.RO Pub Date : 2020-04-06
    Dan Reznik; Ronaldo Garcia

    A Circumconic passes through a triangle's vertices; an Inconic is tangent to the sidelines. We study the variable geometry of certain conics derived from the 1d family of 3-periodics in the Elliptic Billiard. Some display intriguing invariances such as aspect ratio and pairwise ratio of focal lengths. We also define the Circumbilliard, a circumellipse to a generic triangle for which the latter is a

    更新日期:2020-04-08
  • Reinforcement Learning based Visual Navigation with Information-Theoretic Regularization
    arXiv.cs.RO Pub Date : 2019-12-09
    Qiaoyun Wu; Kai Xu; Jun Wang; Mingliang Xu; Dinesh Manocha

    To enhance the cross-target and cross-scene generalization of target-driven visual navigation based on deep reinforcement learning (RL), we introduce an information-theoretic regularization term into the RL objective. The regularization maximizes the mutual information between the action and the current and next visual observations of the agent. This way, the agent understands the causality between

    更新日期:2020-04-08
  • Series Elastic Force Control for Soft Robotic Fluid Actuators
    arXiv.cs.RO Pub Date : 2020-04-02
    Chunpeng Wang; John P. Whitney

    Fluid-based soft actuators are an attractive option for lightweight and human-safe robots. These actuators, combined with fluid pressure force feedback, are in principle a form of series-elastic actuation (SEA), in which nearly all driving-point (e.g. motor/gearbox) friction can be eliminated. Fiber-elastomer soft actuators offer unique low-friction and low-hysteresis mechanical properties which are

    更新日期:2020-04-06
  • RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE
    arXiv.cs.RO Pub Date : 2020-04-02
    Yali Yuan; Robert Tasik; Sripriya Srikant Adhatarao; Yachao Yuan; Zheli Liu; Xiaoming Fu

    With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced

    更新日期:2020-04-06
  • Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors
    arXiv.cs.RO Pub Date : 2020-04-02
    Dominik Notz; Felix Becker; Thomas Kühbeck; Daniel Watzenig

    Collecting realistic driving trajectories is crucial for training machine learning models that imitate human driving behavior. Most of today's autonomous driving datasets contain only a few trajectories per location and are recorded with test vehicles that are cautiously driven by trained drivers. In particular in interactive scenarios such as highway merges, the test driver's behavior significantly

    更新日期:2020-04-06
  • Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
    arXiv.cs.RO Pub Date : 2020-04-03
    Wang Zhao; Shaohui Liu; Yezhi Shu; Yong-Jin Liu

    In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth and pose can be learned across all input samples, which makes the learning problem harder, resulting in degraded performance and limited generalization in indoor environments and long-sequence visual odometry application. To

    更新日期:2020-04-06
  • Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform
    arXiv.cs.RO Pub Date : 2020-04-03
    Alexander Carballo; Abraham Monrroy; David Wong; Patiphon Narksri; Jacob Lambert; Yuki Kitsukawa; Eijiro Takeuchi; Shinpei Kato; Kazuya Takeda

    In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware. LiDAR data used in this study is a subset of our LiDAR Benchmarking

    更新日期:2020-04-06
  • VGPN: Voice-Guided Pointing Robot Navigation for Humans
    arXiv.cs.RO Pub Date : 2020-04-03
    Jun Hu; Zhongyu Jiang; Xionghao Ding; Peter Hall; Taijiang Mu

    Pointing gestures are widely used in robot navigationapproaches nowadays. However, most approaches only use point-ing gestures, and these have two major limitations. Firstly, they need to recognize pointing gestures all the time, which leads to long processing time and significant system overheads. Secondly,the user's pointing direction may not be very accurate, so the robot may go to an undesired

    更新日期:2020-04-06
  • Non-Prehensile Manipulation in Clutter with Human-In-The-Loop
    arXiv.cs.RO Pub Date : 2019-04-07
    Rafael Papallas; Mehmet R. Dogar

    We propose a human-operator guided planning approach to pushing-based manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning. The problem, however, remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based

    更新日期:2020-04-06
  • OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research
    arXiv.cs.RO Pub Date : 2019-10-18
    Ashish Kumar; Toby Buckley; Qiaozhi Wang; Alicia Kavelaars; Ilya Kuzovkin

    Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves. The ultimate challenge of reinforcement learning research is to train real agents to operate in the real environment, but until now there

    更新日期:2020-04-06
  • Generalized Flexible Hybrid Cable-Driven Robot (HCDR): Modeling, Control, and Analysis
    arXiv.cs.RO Pub Date : 2019-11-14
    Ronghuai Qi; Amir Khajepour; William W. Melek

    This paper presents a generalized flexible Hybrid Cable-Driven Robot (HCDR). For the proposed HCDR, the derivation of the equations of motion and proof provide a very effective way to find items for generalized system modeling. The proposed dynamic modeling approach avoids the drawback of traditional methods and can be easily extended to other types of hybrid robots, such as a robot arm mounted on

    更新日期:2020-04-06
  • UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
    arXiv.cs.RO Pub Date : 2019-12-19
    Weisong Wen; Yiyang Zhou; Guohao Zhang; Saman Fahandezh-Saadi; Xiwei Bai; Wei Zhan; Masayoshi Tomizuka; Li-Ta Hsu

    Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field. Beyond Global Navigation Satellite System (GNSS), research in point cloud registration, visual feature matching, and inertia navigation has greatly enhanced the accuracy and robustness of mapping and localization in different scenarios. However, highly urbanized scenes

    更新日期:2020-04-06
  • Synchronizing Probability Measures on Rotations via Optimal Transport
    arXiv.cs.RO Pub Date : 2020-04-01
    Tolga Birdal; Michael Arbel; Umut Şimşekli; Leonidas Guibas

    We introduce a new paradigm, $\textit{measure synchronization}$, for synchronizing graphs with measure-valued edges. We formulate this problem as maximization of the cycle-consistency in the space of probability measures over relative rotations. In particular, we aim at estimating marginal distributions of absolute orientations by synchronizing the $\textit{conditional}$ ones, which are defined on

    更新日期:2020-04-03
  • A Sensorized Multicurved Robot Finger with Data-driven Touch Sensing via Overlapping Light Signals
    arXiv.cs.RO Pub Date : 2020-04-01
    Pedro Piacenza; Keith Behrman; Benedikt Schifferer; Ioannis Kymissis; Matei Ciocarlie

    Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple challenges, including difficulty in covering multicurved surfaces, high wire count, or packaging constrains preventing their use in dexterous hands. In this

    更新日期:2020-04-03
  • Robots in the Danger Zone: Exploring Public Perception through Engagement
    arXiv.cs.RO Pub Date : 2020-04-01
    David A. Robb; Muneeb I. Ahmad; Carlo Tiseo; Simona Aracri; Alistair C. McConnell; Vincent Page; Christian Dondrup; Francisco J. Chiyah Garcia; Hai-Nguyen Nguyen; Èric Pairet; Paola Ardón Ramírez; Tushar Semwal; Hazel M. Taylor; Lindsay J. Wilson; David Lane; Helen Hastie; Katrin Lohan

    Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration

    更新日期:2020-04-03
  • Constrained-Space Optimization and Reinforcement Learning for Complex Tasks
    arXiv.cs.RO Pub Date : 2020-04-01
    Ya-Yen Tsai; Bo Xiao; Edward Johns; Guang-Zhong Yang

    Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space

    更新日期:2020-04-03
  • Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment
    arXiv.cs.RO Pub Date : 2020-04-02
    Frances Zhu; D. Sawyer Elliott; ZhiDi Yang; Haoyuan Zheng

    Exploring and traversing extreme terrain with surface robots is difficult, but highly desirable for many applications, including exploration of planetary surfaces, search and rescue, among others. For these applications, to ensure the robot can predictably locomote, the interaction between the terrain and vehicle, terramechanics, must be incorporated into the model of the robot's locomotion. Modeling

    更新日期:2020-04-03
  • Learning Agile Robotic Locomotion Skills by Imitating Animals
    arXiv.cs.RO Pub Date : 2020-04-02
    Xue Bin Peng; Erwin Coumans; Tingnan Zhang; Tsang-Wei Lee; Jie Tan; Sergey Levine

    Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many complex behaviors, building such controllers involves a time-consuming and difficult development process, often requiring substantial expertise of the nuances of each skill. Reinforcement learning provides an appealing alternative

    更新日期:2020-04-03
  • Exploration of Reinforcement Learning for Event Camera using Car-like Robots
    arXiv.cs.RO Pub Date : 2020-04-02
    Riku Arakawa; Shintaro Shiba

    We demonstrate the first reinforcement-learning application for robots equipped with an event camera. Because of the considerably lower latency of the event camera, it is possible to achieve much faster control of robots compared with the existing vision-based reinforcement-learning applications using standard cameras. To handle a stream of events for reinforcement learning, we introduced an image-like

    更新日期:2020-04-03
  • Enabling End-Users to Deploy Flexible Human-Robot Teams to Factories of the Future
    arXiv.cs.RO Pub Date : 2020-04-02
    Dominik Riedelbauch; Johannes Hartwig; Dominik Henrich

    Human-Robot Teams offer the flexibility needed for partial automation in small and medium-sized enterprises (SMEs). They will thus be an integral part of Factories of the Future. Our research targets a particularly flexible teaming mode, where agents share tasks dynamically. Such approaches require cognitive robots with reasoning and sensing capabilities. This results in hardware maintenance demands

    更新日期:2020-04-03
  • A reconfigurable robot workcell for quick set-up of assembly processes
    arXiv.cs.RO Pub Date : 2020-04-02
    Timotej Gašpar; Miha Deniša; Aleš Ude

    High volume production has been a prerequisite in order to invest into automation of the manufacturing process for decades. The high cost of setup and the inflexibility of classical automation meant that low batch productions, often present in Small and Medium-sized Enterprises (SMEs), were dismissed as potential end user of automation technologies. In this extended abstract we present the results

    更新日期:2020-04-03
  • CLASH WRIST -- A hardware to increase the capability of CLASH fruit gripper to use environment constraints exploration
    arXiv.cs.RO Pub Date : 2020-04-02
    Werner Friedl; Maximo A. Roa

    Humans use environmental constraints (EC) in manipulation to compensate for uncertainties in their world model. The same principle was recently applied to robotics, so that soft underactuated hands improve their grasping capability by using environmental constraints exploitation (ECE) [1]. Due to orientation of the robotic hand for example in the EC wall grasp, the length of the robot wrist plus the

    更新日期:2020-04-03
  • Go Fetch: Mobile Manipulation in Unstructured Environments
    arXiv.cs.RO Pub Date : 2020-04-02
    Kenneth Blomqvist; Michel Breyer; Andrei Cramariuc; Julian Förster; Margarita Grinvald; Florian Tschopp; Jen Jen Chung; Lionel Ott; Juan Nieto; Roland Siegwart

    With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations. Mobile robotics can offer solutions with a high degree of mobility and dexterity, however these complex systems require a multitude of heterogeneous components to be carefully

    更新日期:2020-04-03
  • Human-Guided Planner for Non-Prehensile Manipulation
    arXiv.cs.RO Pub Date : 2020-04-02
    Rafael Papallas; Mehmet R. Dogar

    We present a human-guided planner for non-prehensile manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning, however, the problem remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning

    更新日期:2020-04-03
  • Near Optimality and Tractability in Stochastic Nonlinear Control
    arXiv.cs.RO Pub Date : 2020-04-01
    Mohamed Naveed Gul Mohamed; Suman Chakravorty

    We consider the problem of nonlinear stochastic optimal control. This is fundamentally intractable owing to Bellman's infamous "curse of dimensionality". We present a "decoupling principle" for the tractable feedback design for such problems, wherein, first, a nominal open-loop problem is solved, followed by a suitable linear feedback design around the open-loop. The performance of the resulting feedback

    更新日期:2020-04-03
  • Safe Feedback Motion Planning: A Contraction Theory and $\mathcal{L}_1$-Adaptive Control Based Approach
    arXiv.cs.RO Pub Date : 2020-04-02
    Arun Lakshmanan; Aditya Gahlawat; Naira Hovakimyan

    Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this paper, we present a planner-agnostic framework to design and certify safe tubes around desired trajectories that the robot is always guaranteed to remain inside of. By leveraging recent results in contraction analysis and $\

    更新日期:2020-04-03
  • Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
    arXiv.cs.RO Pub Date : 2020-04-02
    Zackory Erickson; Eliot Xing; Bharat Srirangam; Sonia Chernova; Charles C. Kemp

    Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multimodal sensing technique, leveraging near-infrared spectroscopy and close-range high resolution texture imaging, that enables robots to estimate the materials of household objects. We release a dataset of high resolution texture images and spectral measurements

    更新日期:2020-04-03
  • Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
    arXiv.cs.RO Pub Date : 2018-01-15
    Matej Hoffmann; Rolf Pfeifer

    A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables

    更新日期:2020-04-03
  • An Inverse Dynamics Approach to Control Lyapunov Functions
    arXiv.cs.RO Pub Date : 2019-10-23
    Jenna Reher; Claudia Kann; Aaron D. Ames

    With the goal of moving towards implementation of increasingly dynamic behaviors on underactuated systems, this paper presents an optimization-based approach for solving full-body dynamics based controllers on underactuated bipedal robots. The primary focus of this paper is on the development of an alternative approach to the implementation of controllers utilizing control Lyapunov function based quadratic

    更新日期:2020-04-03
  • A Continuous Teleoperation Subspace with Empirical and Algorithmic Mapping Algorithms for Non-Anthropomorphic Hands
    arXiv.cs.RO Pub Date : 2019-11-21
    Cassie Meeker; Maximilian Haas-Heger; Matei Ciocarlie

    Teleoperation is a valuable tool for robotic manipulators in highly unstructured environments. However, finding an intuitive mapping between a human hand and a non-anthropomorphic robot hand can be difficult, due to the hands' dissimilar kinematics. In this paper, we seek to create a mapping between the human hand and a fully actuated, non-anthropomorphic robot hand that is intuitive enough to enable

    更新日期:2020-04-03
  • CoverNet: Multimodal Behavior Prediction using Trajectory Sets
    arXiv.cs.RO Pub Date : 2019-11-23
    Tung Phan-Minh; Elena Corina Grigore; Freddy A. Boulton; Oscar Beijbom; Eric M. Wolff

    We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse set of trajectories. The size of this set remains manageable due to the limited number

    更新日期:2020-04-03
  • Physically Realizable Adversarial Examples for LiDAR Object Detection
    arXiv.cs.RO Pub Date : 2020-04-01
    James Tu; Mengye Ren; Siva Manivasagam; Ming Liang; Bin Yang; Richard Du; Frank Cheng; Raquel Urtasun

    Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks

    更新日期:2020-04-03
  • Autonomous discovery in the chemical sciences part I: Progress
    arXiv.cs.RO Pub Date : 2020-03-30
    Connor W. Coley; Natalie S. Eyke; Klavs F. Jensen

    This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy

    更新日期:2020-04-01
  • Autonomous discovery in the chemical sciences part II: Outlook
    arXiv.cs.RO Pub Date : 2020-03-30
    Connor W. Coley; Natalie S. Eyke; Klavs F. Jensen

    This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated

    更新日期:2020-04-01
  • The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks
    arXiv.cs.RO Pub Date : 2020-03-30
    Federico Rossi; Tiago Stegun Vaquero; Marc Sanchez Net; Maíra Saboia da Silva; and Joshua Vander Hook

    We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages),

    更新日期:2020-04-01
  • Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
    arXiv.cs.RO Pub Date : 2020-03-30
    Qingrui Zhang; Wei Pan; Vasso Reppa

    This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional control method with deep reinforcement learning. With the conventional control, we can ensure the learning-based control law provides closed-loop stability for the overall system, and potentially increase the sample efficiency of the

    更新日期:2020-04-01
  • Autonomous Cave Surveying with an Aerial Robot
    arXiv.cs.RO Pub Date : 2020-03-31
    Wennie TabibCarnegie Mellon University; Kshitij GoelCarnegie Mellon University; John YaoCarnegie Mellon University; Curtis BoirumCarnegie Mellon University; Nathan MichaelCarnegie Mellon University

    This paper presents a method for cave surveying in complete darkness with an autonomous aerial vehicle equipped with a depth camera for mapping, downward-facing camera for state estimation, and forward and downward lights. Traditional methods of cave surveying are labor-intensive and dangerous due to the risk of hypothermia when collecting data over extended periods of time in cold and damp environments

    更新日期:2020-04-01
  • TITAN: Future Forecast using Action Priors
    arXiv.cs.RO Pub Date : 2020-03-31
    Srikanth Malla; Behzad Dariush; Chiho Choi

    We consider the problem of predicting the future trajectory of scene agents from egocentric views obtained from a moving platform. This problem is important in a variety of domains, particularly for autonomous systems making reactive or strategic decisions in navigation. In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model

    更新日期:2020-04-01
  • Robust Multiple-Path Orienteering Problem: Securing Against Adversarial Attacks
    arXiv.cs.RO Pub Date : 2020-03-31
    Guangyao Shi; Lifeng Zhou; Pratap Tokekar

    The multiple-path orienteering problem asks for paths for a team of robots that maximize the total reward collected while satisfying budget constraints on the path length. This problem models many multi-robot routing tasks such as exploring unknown environments and information gathering for environmental monitoring. In this paper, we focus on how to make the robot team robust to failures when operating

    更新日期:2020-04-01
  • EvolveGraph: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction with Evolving Interaction Graphs
    arXiv.cs.RO Pub Date : 2020-03-31
    Jiachen Li; Fan Yang; Masayoshi Tomizuka; Chiho Choi

    Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the environment and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision and planning. In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph)

    更新日期:2020-04-01
  • Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene
    arXiv.cs.RO Pub Date : 2020-03-31
    Jilin Mei; Huijing Zhao

    We propose a graph neural network(GNN) based method to incorporate scene context for the semantic segmentation of 3D LiDAR data. The problem is defined as building a graph to represent the topology of a center segment with its neighborhoods, then inferring the segment label. The node of graph is generated from the segment on range image, which is suitable for both sparse and dense point cloud. Edge

    更新日期:2020-04-01
  • Cross Scene Prediction via Modeling Dynamic Correlation using Latent Space Shared Auto-Encoders
    arXiv.cs.RO Pub Date : 2020-03-31
    Shaochi Hu; Donghao Xu; Huijing Zhao

    This work addresses on the following problem: given a set of unsynchronized history observations of two scenes that are correlative on their dynamic changes, the purpose is to learn a cross-scene predictor, so that with the observation of one scene, a robot can onlinely predict the dynamic state of another. A method is proposed to solve the problem via modeling dynamic correlation using latent space

    更新日期:2020-04-01
  • Indoor Distance Estimation using LSTMs over WLAN Network
    arXiv.cs.RO Pub Date : 2020-03-31
    Pranav Sankhe; Saqib Azim; Sachin Goyal; Tanya Choudhary; Kumar Appaiah; Sukumar Srikant

    The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy degradation and are almost unavailable in indoor environments. Indoor positioning systems (IPS) based on WiFi signals have been gaining popularity. However, owing to the strong spatial and temporal variations of wireless communication channels in the indoor environment, the achieved accuracy of existing IPS is around several

    更新日期:2020-04-01
  • HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation
    arXiv.cs.RO Pub Date : 2020-03-31
    Pin-Chu Yang; Mohammed Al-Sada; Chang-Chieh Chiu; Kevin Kuo; Tito Pradhono Tomo; Kanata Suzuki; Nelson Yalta; Kuo-Hao Shu; Tetsuya Ogata

    Japanese character figurines are popular and have pivot position in Otaku culture. Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine. Therefore, we take the first steps to bridge this gap by developing Hatsuki, which is a humanoid robot platform with anime based design. Hatsuki's novelty lies in aesthetic design, 2D facial

    更新日期:2020-04-01
  • Can underwater robotics technology save submerged Cultural Heritage?
    arXiv.cs.RO Pub Date : 2020-03-31
    R. dell'Erba; C. Moriconi; A. Trocciola

    Cultural Heritage can largely profit from the set of technologies that have recently been developed in Submarine Robotics. In this paper we focus on how underwater robotics and related technologies can be used to enhance economical fruition, control, protection and social impact of the cultural heritage. Robots allow on-line experience, in remote locations, realizing the remote museum concept as extension

    更新日期:2020-04-01
  • Metric Monocular Localization Using Signed Distance Fields
    arXiv.cs.RO Pub Date : 2020-03-31
    Huaiyang Huang; Yuxiang Sun; Haoyang Ye; Ming Liu

    Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging

    更新日期:2020-04-01
  • GAST-Net: Graph Attention Spatio-temporal Convolutional Networks for 3D Human Pose Estimation in Video
    arXiv.cs.RO Pub Date : 2020-03-11
    Junfa Liu; Yisheng Guang; Juan Rojas

    3D pose estimation in video can benefit greatly from both temporal and spatial information. Occlusions and depth ambiguities remain outstanding problems. In this work, we study how to learn the kinematic constraints of the human skeleton by modeling additional spatial information through attention and interleaving it in a synergistic way with temporal models. We contribute a graph attention spatio-temporal

    更新日期:2020-04-01
  • TartanAir: A Dataset to Push the Limits of Visual SLAM
    arXiv.cs.RO Pub Date : 2020-03-31
    Wenshan Wang; Delong Zhu; Xiangwei Wang; Yaoyu Hu; Yuheng Qiu; Chen Wang; Yafei Hu; Ashish Kapoor; Sebastian Scherer

    We present a challenging dataset, the TartanAir, for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects. By collecting data in simulation, we are able to obtain multi-modal sensor data and precise ground truth labels, including the stereo RGB image, depth image, segmentation, optical

    更新日期:2020-04-01
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