当前期刊: arXiv - CS - Robotics Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
    arXiv.cs.RO Pub Date : 2020-09-25
    Yuke Zhu; Josiah Wong; Ajay Mandlekar; Roberto Martín-Martín

    robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine. It offers a modular design for creating robotic tasks as well as a suite of benchmark environments for reproducible research. This paper discusses the key system modules and the benchmark environments of our new release robosuite v1.0.

    更新日期:2020-09-28
  • SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
    arXiv.cs.RO Pub Date : 2020-09-25
    Juncong Fei; Wenbo Chen; Philipp Heidenreich; Sascha Wirges; Christoph Stiller

    3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks

    更新日期:2020-09-28
  • A Modeled Approach for Online Adversarial Test of Operational Vehicle Safety
    arXiv.cs.RO Pub Date : 2020-09-25
    Linda Capito; Bowen Weng; Umit Ozguner; Keith Redmill

    A scenario-based test of operational vehicle safety presents a set of principal other vehicle (POV) trajectories that seek to enforce the subject vehicle (SV) into a certain safety-critical situation. Current scenarios are mostly (i) statistics-driven: inspired by human driver crash data, (ii) deterministic: POV trajectories are pre-determined and are independent of SV responses, and (iii) overly simplified:

    更新日期:2020-09-28
  • With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
    arXiv.cs.RO Pub Date : 2020-09-25
    Álvaro Serra-Gómez; Bruno Brito; Hai Zhu; Jen Jen Chung; Javier Alonso-Mora

    Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by communication is critical to avoid collisions between cooperating robots. However, the risk of collision between

    更新日期:2020-09-28
  • On Two-Handed Planar Assembly Partitioning
    arXiv.cs.RO Pub Date : 2020-09-25
    Pankaj K. Agarwal; Boris Aronov; Tzvika Geft; Dan Halperin

    Assembly planning, which is a fundamental problem in robotics and automation, aims to design a sequence of motions that will bring the separate constituent parts of a product into their final placement in the product. It is convenient to study assembly planning in reverse order, where the following key problem, assembly partitioning, arises: Given a set of parts in their final placement in a product

    更新日期:2020-09-28
  • Minimum-Violation Planning for Autonomous Systems: Theoretical and Practical Considerations
    arXiv.cs.RO Pub Date : 2020-09-24
    Tichakorn Wongpiromsarn; Konstantin Slutsky; Emilio Frazzoli; Ufuk Topcu

    This paper considers the problem of computing an optimal trajectory for an autonomous system that is subject to a set of potentially conflicting rules. First, we introduce the concept of prioritized safety specifications, where each rule is expressed as a temporal logic formula with its associated weight and priority. The optimality is defined based on the violation of such prioritized safety specifications

    更新日期:2020-09-28
  • Virtual Forward Dynamics Models for Cartesian Robot Control
    arXiv.cs.RO Pub Date : 2020-09-24
    Stefan Scherzinger; Arne Roennau; Rüdiger Dillmann

    In industrial context, admittance control represents an important scheme in programming robots for interaction tasks with their environments. Those robots usually implement high-gain disturbance rejection on joint-level and hide direct access to the actuators behind velocity or position controlled interfaces. Using wrist force-torque sensors to add compliance to these systems, force-resolved control

    更新日期:2020-09-28
  • Deep Reinforcement Learning with Stage Incentive Mechanism for Robotic Trajectory Planning
    arXiv.cs.RO Pub Date : 2020-09-25
    Jin Yang; Gang Peng

    To improve the efficiency of deep reinforcement learning (DRL) based methods for robot manipulator trajectory planning in random working environment. Different from the traditional sparse reward function, we present three dense reward functions in this paper. Firstly, posture reward function is proposed to accelerate the learning process with a more reasonable trajectory by modeling the distance and

    更新日期:2020-09-28
  • Continual Model-Based Reinforcement Learning with Hypernetworks
    arXiv.cs.RO Pub Date : 2020-09-25
    Yizhou Huang; Kevin Xie; Homanga Bharadhwaj; Florian Shkurti

    Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model. In many instances of MBRL and MPC, this model is assumed to be stationary and is periodically re-trained from scratch on state transition experience collected from the beginning of environment interactions. This implies that the time required to train

    更新日期:2020-09-28
  • Goal-Directed Occupancy Prediction for Lane-Following Actors
    arXiv.cs.RO Pub Date : 2020-09-06
    Poornima Kaniarasu; Galen Clark Haynes; Micol Marchetti-Bowick

    Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter

    更新日期:2020-09-28
  • daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery
    arXiv.cs.RO Pub Date : 2020-09-24
    Yidan Qin; Seyedshams Feyzabadi; Max Allan; Joel W. Burdick; Mahdi Azizian

    This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted surgeries (RAS) using multiple input sources. Such predictions are a necessary first step towards shared control and supervised autonomy of surgical subtasks. Minute-long surgical subtasks, such as suturing or ultrasound scanning

    更新日期:2020-09-28
  • A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward
    arXiv.cs.RO Pub Date : 2020-09-24
    M. Ugur Yavas; N. Kemal Ure; Tufan Kumbasar

    Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature. This paper presents the novel deployment of the state of art Q learning method, namely Rainbow DQN, that uses a new safety driven rewarding scheme to tackle the issues in an dynamic and uncertain simulation environment. We present various

    更新日期:2020-09-28
  • Toward Adaptive Trust Calibration for Level 2 Driving Automation
    arXiv.cs.RO Pub Date : 2020-09-24
    Kumar Akash; Neera Jain; Teruhisa Misu

    Properly calibrated human trust is essential for successful interaction between humans and automation. However, while human trust calibration can be improved by increased automation transparency, too much transparency can overwhelm human workload. To address this tradeoff, we present a probabilistic framework using a partially observable Markov decision process (POMDP) for modeling the coupled trust-workload

    更新日期:2020-09-28
  • Learning Equality Constraints for Motion Planning on Manifolds
    arXiv.cs.RO Pub Date : 2020-09-24
    Giovanni Sutanto; Isabel M. Rayas Fernández; Peter Englert; Ragesh K. Ramachandran; Gaurav S. Sukhatme

    Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN). The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion

    更新日期:2020-09-25
  • Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles
    arXiv.cs.RO Pub Date : 2020-09-24
    Sanghyun Kim; Jongmin Park; Jae-Kwan Yun; Jiwon Seo

    In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment

    更新日期:2020-09-25
  • Evaluation of an indoor localization system for a mobile robot
    arXiv.cs.RO Pub Date : 2020-09-24
    Victor J. Exposito Jimenez; Christian Schwarzl; Helmut Martin

    Although indoor localization has been a wide researched topic, obtained results may not fit the requirements that some domains need. Most approaches are not able to precisely localize a fast moving object even with a complex installation, which makes their implementation in the automated driving domain complicated. In this publication, common technologies were analyzed and a commercial product, called

    更新日期:2020-09-25
  • Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics
    arXiv.cs.RO Pub Date : 2020-09-24
    Yanshi Luo; Abdeslam Boularias; Mridul Aanjaneya

    We present the design of a low-cost wheeled mobile robot, and an analytical model for predicting its motion under the influence of motor torques and friction forces. Using our proposed model, we show how to analytically compute the gradient of an appropriate loss function, that measures the deviation between predicted motion trajectories and real-world trajectories, which are estimated using Apriltags

    更新日期:2020-09-25
  • TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment
    arXiv.cs.RO Pub Date : 2020-09-23
    Runxin He; Jinyun Zhou; Shu Jiang; Yu Wang; Jiaming Tao; Shiyu Song; Jiangtao Hu; Jinghao Miao; Qi Luo

    This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robust-ness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduces two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulates the problem to improve

    更新日期:2020-09-25
  • Neural Identification for Control
    arXiv.cs.RO Pub Date : 2020-09-24
    Priyabrata Saha; Saibal Mukhopadhyay

    We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The open-loop input-output behavior of the underlying dynamical system is used as the supervising signal

    更新日期:2020-09-25
  • 3D Object Localization Using 2D Estimates for Computer Vision Applications
    arXiv.cs.RO Pub Date : 2020-09-24
    Taha Hasan Masood Siddique; Muhammad Usman

    A technique for object localization based on pose estimation and camera calibration is presented. The 3-dimensional (3D) coordinates are estimated by collecting multiple 2-dimensional (2D) images of the object and are utilized for the calibration of the camera. The calibration steps involving a number of parameter calculation including intrinsic and extrinsic parameters for the removal of lens distortion

    更新日期:2020-09-25
  • A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
    arXiv.cs.RO Pub Date : 2020-09-23
    Florian Wirthmüller; Marvin Klimke; Julian Schlechtriemen; Jochen Hipp; Manfred Reichert

    Already today, driver assistance systems help to make daily traffic more comfortable and safer. However, there are still situations that are quite rare but are hard to handle at the same time. In order to cope with these situations and to bridge the gap towards fully automated driving, it becomes necessary to not only collect enormous amounts of data but rather the right ones. This data can be used

    更新日期:2020-09-24
  • Dual-SLAM: A framework for robust single camera navigation
    arXiv.cs.RO Pub Date : 2020-09-23
    Huajian Huang; Wen-Yan Lin; Siying Liu; Dong Zhang; Sai-Kit Yeung

    SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle

    更新日期:2020-09-24
  • Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
    arXiv.cs.RO Pub Date : 2020-09-23
    Péter Almási; Róbert Moni; Bálint Gyires-Tóth

    Deep Reinforcement Learning (DRL) has been successfully used to solve different challenges, e.g. complex board and computer games, recently. However, solving real-world robotics tasks with DRL seems to be a more difficult challenge. The desired approach would be to train the agent in a simulator and transfer it to the real world. Still, models trained in a simulator tend to perform poorly in real-world

    更新日期:2020-09-24
  • ContactNets: Learning of Discontinuous Contact Dynamics with Smooth, Implicit Representations
    arXiv.cs.RO Pub Date : 2020-09-23
    Samuel Pfrommer; Mathew Halm; Michael Posa

    Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior. In this work, we resolve this conflict with a smooth, implicit encoding of the structure inherent to contact-induced discontinuities. Our method, ContactNets, learns parameterizations of inter-body signed distance and contact-frame

    更新日期:2020-09-24
  • DL-IAPS and PJSO: A Path/Speed Decoupled Trajectory Optimization and its Application in Autonomous Driving
    arXiv.cs.RO Pub Date : 2020-09-23
    Jinyun Zhou; Runxin He; Yu Wang; Shu Jiang; Zhenguang Zhu; Jiangtao Hu; Jinghao Miao; Qi Luo

    This paper presents a free space trajectory optimization algorithm of autonomous driving vehicle, which decouples the collision-free trajectory planning problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) and a Piece-wise Jerk Speed Optimization (PJSO). The work leads to remarkable driving performance improvements including more precise collision avoidance, higher control feasibility

    更新日期:2020-09-24
  • PICs for TECH: Pose Imitation Constraints (PICs) for TEaching Collaborative Heterogeneous robots (TECH)
    arXiv.cs.RO Pub Date : 2020-09-23
    Glebys Gonzalez; Juan Wachs

    Achieving human-like motion in robots has been a fundamental goal in many areas of robotics research. Inverse kinematic (IK) solvers have been explored as a solution to provide kinematic structures with anthropomorphic movements. In particular, numeric solvers based on geometry, such as FABRIK, have shown potential for producing human-like motion at a low computational cost. Nevertheless, these methods

    更新日期:2020-09-24
  • Behavioral Repertoires for Soft Tensegrity Robots
    arXiv.cs.RO Pub Date : 2020-09-23
    Kyle Doney; Aikaterini Petridou; Jacob Karaul; Ali Khan; Geoffrey Liu; John Rieffel

    Mobile soft robots offer compelling applications in fields ranging from urban search and rescue to planetary exploration. A critical challenge of soft robotic control is that the nonlinear dynamics imposed by soft materials often result in complex behaviors that are counterintuitive and hard to model or predict. As a consequence, most behaviors for mobile soft robots are discovered through empirical

    更新日期:2020-09-24
  • Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks
    arXiv.cs.RO Pub Date : 2020-09-22
    Rui Yu; Zhenyuan Yuan; Minghui Zhu; Zihan Zhou

    Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning. However, tracking real-world objects often faces two key challenges: First, due to the limitation of individual sensors, state estimation needs to be solved in a collaborative and distributed manner

    更新日期:2020-09-24
  • Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation
    arXiv.cs.RO Pub Date : 2020-09-23
    Dimche Kostadinov; Davide Scaramuzza

    Event-based cameras record an asynchronous stream of per-pixel brightness changes. As such, they have numerous advantages over the standard frame-based cameras, including high temporal resolution, high dynamic range, and no motion blur. Due to the asynchronous nature, efficient learning of compact representation for event data is challenging. While it remains not explored the extent to which the spatial

    更新日期:2020-09-24
  • Factor Graph-Based Smoothing Without Matrix Inversion for Highly Precise Localization
    arXiv.cs.RO Pub Date : 2020-09-22
    Paul ChauchatISAE-SUPAERO; Axel BarrauCAOR; Silvère BonnabelCAOR

    We consider the problem of localizing a manned, semi-autonomous, or autonomous vehicle in the environment using information coming from the vehicle's sensors, a problem known as navigation or simultaneous localization and mapping (SLAM) depending on the context. To infer knowledge from sensors' measurements, while drawing on a priori knowledge about the vehicle's dynamics, modern approaches solve an

    更新日期:2020-09-24
  • An Intuitive Tutorial to Gaussian Processes Regression
    arXiv.cs.RO Pub Date : 2020-09-22
    Jie Wang

    This introduction aims to provide readers an intuitive understanding of Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because their representation flexibility and inherently uncertainty measures over predictions. The paper starts with explaining mathematical basics that Gaussian processes built on including multivariate

    更新日期:2020-09-24
  • Angular Luminance for Material Segmentation
    arXiv.cs.RO Pub Date : 2020-09-22
    Jia Xue; Matthew Purri; Kristin Dana

    Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of a moving camera sequence, a distribution of intensities over multiple angles is obtained. It is well known from prior work that luminance histograms and the statistics of natural images

    更新日期:2020-09-24
  • Ants, robots, humans: a self-organizing, goal-driven modeling approach
    arXiv.cs.RO Pub Date : 2020-09-21
    Martin Jaraiz

    Most of the grand challenges of humanity today involve complex agent-based systems, such as epidemiology, economics or ecology. However, remains as a pending task the challenge of identifying the general principles underlying the self-organizing capabilities of those complex systems. This article presents a novel modeling approach capable to self-deploy both the system structure and the activities

    更新日期:2020-09-24
  • A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods
    arXiv.cs.RO Pub Date : 2020-09-22
    Jonathan D. Gammell; Marlin P. Strub

    Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have

    更新日期:2020-09-23
  • Self-Adapting Variable Impedance Actuator Control for Precision and Dynamic Tasks
    arXiv.cs.RO Pub Date : 2020-09-22
    Manuel Aiple; Andre Schiele; Frans C. T. van der Helm

    Variable impedance actuators (VIAs) as tool devices for teleoperation could extend the range of tasks that humans can perform through a teleoperated robot by mimicking the change of upper limb stiffness that humans perform for different tasks, increasing the dynamic range of the robot. This requires appropriate impedance control. Goal of this study is to show the effectiveness of a controller that

    更新日期:2020-09-23
  • Fail-Safe Controller Architectures for Quadcopter with Motor Failures
    arXiv.cs.RO Pub Date : 2020-09-22
    Gene Patrick S. Rible; Nicolette Ann A. Arriola; Manuel C. Ramos, Jr

    A fail-safe algorithm in case of motor failure was developed, simulated, and tested. For practical fail-safe flight, the quadcopter may fly with only three or two opposing propellers. Altitude for two-propeller architecture was maintained by a PID controller that is independent from the inner and outer controllers. A PID controller on propeller force deviations from equilibrium was augmented to the

    更新日期:2020-09-23
  • Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics
    arXiv.cs.RO Pub Date : 2020-09-21
    S. Banerjee; J. Harrison; P. M. Furlong; M. Pavone

    Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain

    更新日期:2020-09-23
  • Time-of-Flight LiDAR-based Precise Mapping
    arXiv.cs.RO Pub Date : 2020-09-21
    Han Wu; Zhi Yan

    Last two decades, the problem of robotic mapping has made a lot of progress in the research community. However, since the data provided by the sensor still contains noise, how to obtain an accurate map is still an open problem. In this note, we analyze the problem from the perspective of mathematical analysis and propose a probabilistic map update method based on multiple explorations. The proposed

    更新日期:2020-09-23
  • Learning Task-Agnostic Action Spaces for Movement Optimization
    arXiv.cs.RO Pub Date : 2020-09-22
    Amin Babadi; Michiel van de Panne; C. Karen Liu; Perttu Hämäläinen

    We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous papers, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with

    更新日期:2020-09-23
  • End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands
    arXiv.cs.RO Pub Date : 2020-09-22
    Mohsen Jafarzadeh; Yonas Tadesse

    Speech is one of the most common forms of communication in humans. Speech commands are essential parts of multimodal controlling of prosthetic hands. In the past decades, researchers used automatic speech recognition systems for controlling prosthetic hands by using speech commands. Automatic speech recognition systems learn how to map human speech to text. Then, they used natural language processing

    更新日期:2020-09-23
  • Dynamic Multi-Agent Path Finding based on Conflict Resolution using Answer Set Programming
    arXiv.cs.RO Pub Date : 2020-09-22
    Basem AtiqSabanci University; Volkan PatogluSabanci University; Esra ErdemSabanci University

    We study a dynamic version of multi-agent path finding problem (called D-MAPF) where existing agents may leave and new agents may join the team at different times. We introduce a new method to solve D-MAPF based on conflict-resolution. The idea is, when a set of new agents joins the team and there are conflicts, instead of replanning for the whole team, to replan only for a minimal subset of agents

    更新日期:2020-09-23
  • DVI: Depth Guided Video Inpainting for Autonomous Driving
    arXiv.cs.RO Pub Date : 2020-07-17
    Miao Liao; Feixiang Lu; Dingfu Zhou; Sibo Zhang; Wei Li; Ruigang Yang

    To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud. By building a dense 3D map from stitched point clouds, frames within a video are geometrically correlated via this common 3D map. In order to fill a target inpainting

    更新日期:2020-09-23
  • Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
    arXiv.cs.RO Pub Date : 2020-09-21
    Xingye Da; Zhaoming Xie; David Hoeller; Byron Boots; Animashree Anandkumar; Yuke Zhu; Buck Babich; Animesh Garg

    We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-level controller that utilizes an established control method to robustly execute

    更新日期:2020-09-22
  • CMAX++ : Leveraging Experience for Planning and Execution using Inaccurate Models
    arXiv.cs.RO Pub Date : 2020-09-21
    Anirudh Vemula; J. Andrew Bagnell; Maxim Likhachev

    Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult to model the true dynamics of the real world before execution, especially for tasks requiring interactions with objects whose parameters are unknown. A recent planning approach, CMAX, tackles this problem by adapting the planner

    更新日期:2020-09-22
  • Heterogeneous Fixed-wing Aerial Vehicles for Resilient Coverage of an Area
    arXiv.cs.RO Pub Date : 2020-09-17
    Sachin Shriwastav; Zhuoyuan Song

    This paper presents a distributed approach to provide persistent coverage of an arbitrarily shaped area using heterogeneous coverage of fixed-wing unmanned aerial vehicles (UAVs), and to recover from simultaneous failures of multiple UAVs. The proposed approach discusses level-homogeneous deployment and maintenance of a homogeneous fleet of fixed-wing UAVs given the boundary information and the minimum

    更新日期:2020-09-22
  • Grasp-type Recognition Leveraging Object Affordance
    arXiv.cs.RO Pub Date : 2020-08-26
    Naoki Wake; Kazuhiro Sasabuchi; Katsushi Ikeuchi

    A key challenge in robot teaching is grasp-type recognition with a single RGB image and a target object name. Here, we propose a simple yet effective pipeline to enhance learning-based recognition by leveraging a prior distribution of grasp types for each object. In the pipeline, a convolutional neural network (CNN) recognizes the grasp type from an RGB image. The recognition result is further corrected

    更新日期:2020-09-22
  • Reinforcement Learning Approaches in Social Robotics
    arXiv.cs.RO Pub Date : 2020-09-21
    Neziha Akalin; Amy Loutfi

    There is a growing body of literature that formulates social human-robot interactions as sequential decision-making tasks. In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics. This article surveys reinforcement learning approaches in social robotics. We propose a taxonomy that categorizes reinforcement learning

    更新日期:2020-09-22
  • Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains
    arXiv.cs.RO Pub Date : 2020-09-20
    Aniket Shirsat; Karthik Elamvazhuthi; Spring Berman

    In this paper, we propose a probabilistic consensus-based multi-robot search strategy that is robust to communication link failures, and thus is suitable for disaster affected areas. The robots, capable of only local communication, explore a bounded environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and exchange information with neighboring robots,

    更新日期:2020-09-22
  • Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores
    arXiv.cs.RO Pub Date : 2020-09-20
    Tzvika Geft; Aviv Tamar; Ken Goldberg; Dan Halperin

    To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with added stochastic noise to yield robustness scores--the success probabilities of planned assembly motions. As running a simulation for every assembly motion is impractical, we train a convolutional neural network to map assembly

    更新日期:2020-09-22
  • Comparing Feedback Linearization and Adaptive Backstepping Control for Airborne Orientation of Agile Ground Robots using Wheel Reaction Torque
    arXiv.cs.RO Pub Date : 2020-09-20
    Jinho Kim; Daniel J. Gonzalez; Christopher M. Korpela

    In this paper, two nonlinear methods for stabilizing the orientation of a Four-Wheel Independent Drive and Steering (4WIDS) robot while in the air are analyzed, implemented in simulation, and compared. AGRO (the Agile Ground Robot) is a 4WIDS inspection robot that can be deployed into unsafe environments by being thrown, and can use the reaction torque from its four wheels to command its orientation

    更新日期:2020-09-22
  • What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment
    arXiv.cs.RO Pub Date : 2020-09-19
    Filipe Mutz; Thiago Oliveira-Santos; Avelino Forechi; Karin S. Komati; Claudine Badue; Felipe M. G. França; Alberto F. De Souza

    The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps for estimating the car pose. Since maintaining and using several maps is computationally expensive, it is important to analyze which type of map is more adequate for each application. In this work, we provide data for such

    更新日期:2020-09-22
  • Design and Development of a Gecko-Adhesive Gripper for the Astrobee Free-Flying Robot
    arXiv.cs.RO Pub Date : 2020-09-19
    A. Cauligi; T. Chen; S. A. Suresh; M. Dille; R. Garcia Ruiz; A. Mora Vargas; M. Pavone; M. Cutkosky

    Assistive free-flying robots are a promising platform for supporting and working alongside astronauts in carrying out tasks that require interaction with the environment. However, current free-flying robot platforms are limited by existing manipulation technologies in being able to grasp and manipulate surrounding objects. Instead, gecko-inspired adhesives offer many advantages for an alternate grasping

    更新日期:2020-09-22
  • Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining
    arXiv.cs.RO Pub Date : 2020-09-19
    Jekan Thangavelautham; Yinan Xu

    Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and dangerous task. The challenge is to scale up productivity with an ever-increasing number of robots. This paper presents a novel method for developing scalable controllers for use in multi-robot excavation and site-preparation scenarios. The controller starts with a blank slate and does not require human-authored operations scripts

    更新日期:2020-09-22
  • Emotional Musical Prosody for the Enhancement of Trust in Robotic Arm Communication
    arXiv.cs.RO Pub Date : 2020-09-18
    Richard Savery; Lisa Zahray; Gil Weinberg

    As robotic arms become prevalent in industry it is crucial to improve levels of trust from human collaborators. Low levels of trust in human-robot interaction can reduce overall performance and prevent full robot utilization. We investigated the potential benefits of using emotional musical prosody to allow the robot to respond emotionally to the user's actions. We tested participants' responses to

    更新日期:2020-09-22
  • Solution Concepts in Hierarchical Games with Applications to Autonomous Driving
    arXiv.cs.RO Pub Date : 2020-09-21
    Atrisha Sarkar; Krzysztof Czarnecki

    With autonomous vehicles (AV) set to integrate further into regular human traffic, there is an increasing consensus of treating AV motion planning as a multi-agent problem. However, the traditional game theoretic assumption of complete rationality is too strong for the purpose of human driving, and there is a need for understanding human driving as a bounded rational activity through a behavioral game

    更新日期:2020-09-22
  • Line Flow based SLAM
    arXiv.cs.RO Pub Date : 2020-09-21
    Qiuyuan Wang; Zike Yan; Junqiu Wang; Fei Xue; Wei Ma; Hongbin Zha

    We propose a method of visual SLAM by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While indirect SLAM methods using points and line segments have achieved excellent results, they still face problems in challenging scenarios such as occlusions, image blur, and repetitive textures. To deal with these problems, we leverage line flows which encode the

    更新日期:2020-09-22
  • RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots
    arXiv.cs.RO Pub Date : 2020-09-21
    Tamir Blum; Gabin Paillet; Mickael Laine; Kazuya Yoshida

    Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics, something which is challenging with traditional techniques due to stochasticity and uncertainty within the environment. RL can be used to enable lunar cave exploration with infrequent human feedback, faster and safer lunar surface locomotion or the coordination and collaboration

    更新日期:2020-09-22
  • PESAO: Psychophysical Experimental Setup for Active Observers
    arXiv.cs.RO Pub Date : 2020-09-15
    Markus D. Solbach; John K. Tsotsos

    Most past and present research in computer vision involves passively observed data. Humans, however, are active observers outside the lab; they explore, search, select what and how to look. Nonetheless, how exactly active observation occurs in humans so that it can inform the design of active computer vision systems is an open problem. PESAO is designed for investigating active, visual observation

    更新日期:2020-09-22
  • LiPo-LCD: Combining Lines and Points for Appearance-based Loop Closure Detection
    arXiv.cs.RO Pub Date : 2020-09-03
    Joan P. Company-Corcoles; Emilio Garcia-Fidalgo; Alberto Ortiz

    Visual SLAM approaches typically depend on loop closure detection to correct the inconsistencies that may arise during the map and camera trajectory calculations, typically making use of point features for detecting and closing the existing loops. In low-textured scenarios, however, it is difficult to find enough point features and, hence, the performance of these solutions drops drastically. An alternative

    更新日期:2020-09-22
  • Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization
    arXiv.cs.RO Pub Date : 2020-09-21
    Feng Tao; Yongcan Cao

    In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is unknown to the learning agent. One main relevant approach is the inverse reinforcement learning (IRL), which mainly focuses on inferring a reward function from expert demonstrations. The obtained control policy by IRL and

    更新日期:2020-09-22
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
物理学研究前沿热点精选期刊推荐
chemistry
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷
屿渡论文,编辑服务
阿拉丁试剂right
南昌大学
王辉
南方科技大学
彭小水
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
赵延川
李霄羽
廖矿标
朱守非
试剂库存
down
wechat
bug