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  • Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
    arXiv.cs.RO Pub Date : 2020-11-24
    Joanne Truong; Denis Yarats; Tianyu Li; Franziska Meier; Sonia Chernova; Dhruv Batra; Akshara Rai

    Navigation policies are commonly learned on idealized cylinder agents in simulation, without modelling complex dynamics, like contact dynamics, arising from the interaction between the robot and the environment. Such policies perform poorly when deployed on complex and dynamic robots, such as legged robots. In this work, we learn hierarchical navigation policies that account for the low-level dynamics

    更新日期:2020-11-25
  • Achieving Sample-Efficient and Online-Training-Safe Deep Reinforcement Learning with Base Controllers
    arXiv.cs.RO Pub Date : 2020-11-24
    Minjian Xin; Guangming Wang; Zhe Liu; Hesheng Wang

    Application of Deep Reinforcement Learning (DRL) algorithms in real-world robotic tasks faces many challenges. On the one hand, reward-shaping for complex tasks is difficult and may result in sub-optimal performances. On the other hand, a sparse-reward setting renders exploration inefficient, and exploration using physical robots is of high-cost and unsafe. In this paper we propose a method of learning

    更新日期:2020-11-25
  • Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents
    arXiv.cs.RO Pub Date : 2020-11-24
    Daniel Beßler; Robert Porzel; Mihai Pomarlan; Abhijit Vyas; Sebastian Höffner; Michael Beetz; Rainer Malaka; John Bateman

    In this paper, we present foundations of the Socio-physical Model of Activities (SOMA). SOMA represents both the physical as well as the social context of everyday activities. Such tasks seem to be trivial for humans, however, they pose severe problems for artificial agents. For starters, a natural language command requesting something will leave many pieces of information necessary for performing

    更新日期:2020-11-25
  • Path Planning with Automatic Seam Extraction over Point Cloud Models for Robotic Arc Welding
    arXiv.cs.RO Pub Date : 2020-11-24
    Peng Zhou; Rui Peng; Maggie Xu; Victor Wu; David Navarro-Alarcon

    This paper presents a point cloud based robotic system for arc welding. Using hand gesture controls, the system scans partial point cloud views of workpiece and reconstructs them into a complete 3D model by a linear iterative closest point algorithm. Then, a bilateral filter is extended to denoise the workpiece model and preserve important geometrical information. To extract the welding seam from the

    更新日期:2020-11-25
  • Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
    arXiv.cs.RO Pub Date : 2020-11-24
    Ahmadreza Ahmadi; Tønnes Nygaard; Navinda Kottege; David Howard; Nicolas Hudson

    Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling technology for autonomous legged robots, as it allows the robot to harness their innate flexibility to adapt their behaviour to the demands of their operating environment. In this paper, we show how highly capable machine

    更新日期:2020-11-25
  • Rotational Error Metrics for Quadrotor Control
    arXiv.cs.RO Pub Date : 2020-11-24
    Alexander Spitzer; Nathan Michael

    We analyze and experimentally compare various rotational error metrics for use in quadrotor controllers. Traditional quadrotor attitude controllers have used Euler angles or the full rotation to compute an attitude error and scale that to compute a control response. Recently, several works have shown that prioritizing quadrotor tilt, or thrust vector error, in the attitude controller leads to improved

    更新日期:2020-11-25
  • A Robotic Dating Coaching System Leveraging Online Communities Posts
    arXiv.cs.RO Pub Date : 2020-11-24
    Sihyeon Jo; Donghwi Jung; Keonwoo Kim; Eun Gyo Joung; Giulia Nespoli; Seungryong Yoo; Minseob So; Seung-Woo Seo; Seong-Woo Kim

    Can a robot be a personal dating coach? Even with the increasing amount of conversational data on the internet, the implementation of conversational robots remains a challenge. In particular, a detailed and professional counseling log is expensive and not publicly accessible. In this paper, we develop a robot dating coaching system leveraging corpus from online communities. We examine people's perceptions

    更新日期:2020-11-25
  • Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements
    arXiv.cs.RO Pub Date : 2020-11-24
    Ke Sun; Brent Schlotfeldt George Pappas; Vijay Kumar

    In this paper, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a POMDP. Recent work on general purpose POMDP solvers

    更新日期:2020-11-25
  • An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
    arXiv.cs.RO Pub Date : 2020-11-23
    Carlos H. C. Pena; Mateus G. Machado; Mariana S. Barros; José D. P. Silva; Lucas D. Maciel; Tsang Ing Ren; Edna N. S. Barros; Pedro H. M. Braga; Hansenclever F. Bassani

    The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this paper proposes an end-to-end approach for the coaching

    更新日期:2020-11-25
  • Multimodal dynamics modeling for off-road autonomous vehicles
    arXiv.cs.RO Pub Date : 2020-11-23
    Jean-François Tremblay; Travis Manderson; Aurélio Noca; Gregory Dudek; David Meger

    Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot's environment is thus crucial when building a model to perform predictions about the robot's dynamics with the goal of doing motion planning. We design

    更新日期:2020-11-25
  • RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM
    arXiv.cs.RO Pub Date : 2020-11-23
    Tong Ke; Kejian J. Wu; Stergios I. Roumeliotis

    In this paper, we present the RISE-SLAM algorithm for performing visual-inertial simultaneous localization and mapping (SLAM), while improving estimation consistency. Specifically, in order to achieve real-time operation, existing approaches often assume previously-estimated states to be perfectly known, which leads to inconsistent estimates. Instead, based on the idea of the Schmidt-Kalman filter

    更新日期:2020-11-25
  • From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion
    arXiv.cs.RO Pub Date : 2020-11-23
    Deepali Jain; Atil Iscen; Ken Caluwaerts

    Legged robots navigating crowded scenes and complex terrains in the real world are required to execute dynamic leg movements while processing visual input for obstacle avoidance and path planning. We show that a quadruped robot can acquire both of these skills by means of hierarchical reinforcement learning (HRL). By virtue of their hierarchical structure, our policies learn to implicitly break down

    更新日期:2020-11-25
  • Mechanical Search on Shelves using Lateral Access X-RAY
    arXiv.cs.RO Pub Date : 2020-11-23
    Huang Huang; Marcus Dominguez-Kuhne; Jeffrey Ichnowski; Vishal Satish; Michael Danielczuk; Kate Sanders; Andrew Lee; Anelia Angelova; Vincent Vanhoucke; Ken Goldberg

    Efficiently finding an occluded object with lateral access arises in many contexts such as warehouses, retail, healthcare, shipping, and homes. We introduce LAX-RAY (Lateral Access maXimal Reduction of occupancY support Area), a system to automate the mechanical search for occluded objects on shelves. For such lateral access environments, LAX-RAY couples a perception pipeline predicting a target object

    更新日期:2020-11-25
  • Sequential Topological Representations for Predictive Models of Deformable Objects
    arXiv.cs.RO Pub Date : 2020-11-23
    Rika Antonova; Anastasiia Varava; Peiyang Shi; J. Frederico Carvalho; Danica Kragic

    Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting, and controlling such objects. We construct compact topological representations to capture the state of highly deformable objects that are topologically nontrivial. We develop an approach that tracks the evolution of this

    更新日期:2020-11-25
  • Multi-Stage CNN-Based Monocular 3D Vehicle Localization and Orientation Estimation
    arXiv.cs.RO Pub Date : 2020-11-24
    Ali Babolhavaeji; Mohammad Fanaei

    This paper aims to design a 3D object detection model from 2D images taken by monocular cameras by combining the estimated bird's-eye view elevation map and the deep representation of object features. The proposed model has a pre-trained ResNet-50 network as its backend network and three more branches. The model first builds a bird's-eye view elevation map to estimate the depth of the object in the

    更新日期:2020-11-25
  • SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
    arXiv.cs.RO Pub Date : 2020-11-24
    Sheng Ao; Qingyong Hu; Bo Yang; Andrew Markham; Yulan Guo

    Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet

    更新日期:2020-11-25
  • Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
    arXiv.cs.RO Pub Date : 2020-11-24
    Daisuke Nishiyama; Mario Ynocente Castro; Shirou Maruyama; Shinya Shiroshita; Karim Hamzaoui; Yi Ouyang; Guy Rosman; Jonathan DeCastro; Kuan-Hui Lee; Adrien Gaidon

    Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the core decision-making component of autonomous robots: their planning algorithm. We introduce a planner testing framework that leverages recent progress in simulating behaviorally diverse traffic participants. Using large scale

    更新日期:2020-11-25
  • REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning
    arXiv.cs.RO Pub Date : 2020-11-24
    Yunzhe Tao; Sahika Genc; Tao Sun; Sunil Mallya

    Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown. In this work, we propose a REPresentation-And-INstance Transfer algorithm (REPAINT) for deep actor-critic reinforcement learning paradigm. In representation

    更新日期:2020-11-25
  • Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components
    arXiv.cs.RO Pub Date : 2020-11-23
    Joseph A. Vincent; Mac Schwager

    We present a method for computing exact reachable sets for deep neural networks with rectified linear unit (ReLU) activation. Our method is well-suited for use in rigorous safety analysis of robotic perception and control systems with deep neural network components. Our algorithm can compute both forward and backward reachable sets for a ReLU network iterated over multiple time steps, as would be found

    更新日期:2020-11-25
  • MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze
    arXiv.cs.RO Pub Date : 2020-11-23
    Philipp Kratzer; Simon Bihlmaier; Niteesh Balachandra Midlagajni; Rohit Prakash; Marc Toussaint; Jim Mainprice

    As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include 1) long sequences of manipulation tasks, 2) the 3D model of the workspace geometry, and 3) eye-gaze, which are

    更新日期:2020-11-25
  • Elastic Interaction of Particles for Robotic Tactile Simulation
    arXiv.cs.RO Pub Date : 2020-11-23
    Yikai Wang; Wenbing Huang; Bin Fang; Fuchun Sun

    Tactile sensing plays an important role in robotic perception and manipulation. To overcome the real-world limitations of data collection, simulating tactile response in virtual environment comes as a desire direction of robotic research. Most existing works model the tactile sensor as a rigid multi-body, which is incapable of reflecting the elastic property of the tactile sensor as well as characterizing

    更新日期:2020-11-25
  • The Dynamic of Body and Brain Co-Evolution
    arXiv.cs.RO Pub Date : 2020-11-23
    Paolo Pagliuca; Stefano Nolfi

    We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan or to evolve the morphological bauplan as well. Our results indicate that robots with co-adapted body and control traits outperform robots with fixed hand-designed morphologies. Interestingly, the advantage

    更新日期:2020-11-25
  • Imagination-enabled Robot Perception
    arXiv.cs.RO Pub Date : 2020-11-23
    Patrick Mania; Franklin Kenghagho Kenfack; Michael Neumann; Michael Beetz

    Many of today's robot perception systems aim at accomplishing perception tasks that are too simplistic and too hard. They are too simplistic because they do not require the perception systems to provide all the information needed to accomplish manipulation tasks. Typically the perception results do not include information about the part structure of objects, articulation mechanisms and other attributes

    更新日期:2020-11-25
  • CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
    arXiv.cs.RO Pub Date : 2020-11-23
    Yuewen Zhu; Chunran Zheng; Chongjian Yuan; Xu Huang; Xiaoping Hong

    Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at a large scale outdoor scenario. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2)

    更新日期:2020-11-25
  • COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
    arXiv.cs.RO Pub Date : 2020-11-23
    Zhuo Xu; Wenhao Yu; Alexander Herzog; Wenlong Lu; Chuyuan Fu; Masayoshi Tomizuka; Yunfei Bai; C. Karen Liu; Daniel Ho

    General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics. Deep reinforcement learning (RL) has shown great potential in solving robot manipulation tasks. However, existing RL policies have limited adaptability to environments with diverse dynamics properties, which is pivotal in solving many contact-rich manipulation

    更新日期:2020-11-25
  • Data-driven Holistic Framework for Automated Laparoscope Optimal View Control with Learning-based Depth Perception
    arXiv.cs.RO Pub Date : 2020-11-23
    Bin Li; Bo Lu; Yiang Lu; Qi Dou; Yun-Hui Liu

    Laparoscopic Field of View (FOV) control is one of the most fundamental and important components in Minimally Invasive Surgery (MIS), nevertheless, the traditional manual holding paradigm may easily bring fatigue to surgical assistants, and misunderstanding between surgeons also hinders assistants to provide a high-quality FOV. Targeting this problem, we here present a data-driven framework to realize

    更新日期:2020-11-25
  • Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles
    arXiv.cs.RO Pub Date : 2020-11-23
    Kunming Li; Mao Shan; Karan Narula; Stewart Worrall; Eduardo Nebot

    Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions

    更新日期:2020-11-25
  • Model Predictive Control for Micro Aerial Vehicles: A Survey
    arXiv.cs.RO Pub Date : 2020-11-22
    Huan Nguyen; Mina Kamel; Kostas Alexis; Roland Siegwart

    This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement

    更新日期:2020-11-25
  • Experimental Assessment of Human-Robot Teaming for Multi-Step Remote Manipulation with Expert Operators
    arXiv.cs.RO Pub Date : 2020-11-22
    Claudia Pérez-D'Arpino; Rebecca P. Khurshid; Julie A. Shah

    Remote robot manipulation with human control enables applications where safety and environmental constraints are adverse to humans (e.g. underwater, space robotics and disaster response) or the complexity of the task demands human-level cognition and dexterity (e.g. robotic surgery and manufacturing). These systems typically use direct teleoperation at the motion level, and are usually limited to low-DOF

    更新日期:2020-11-25
  • Control and implementation of fluid-driven soft gripper with dynamic uncertainty of object
    arXiv.cs.RO Pub Date : 2020-11-21
    Amirhosein Alian; Mohammad Zareinejad; Heidar Ali Talebi

    Soft grippers, for stable grasping of objects, with high compliance could be considered a suitable candidate for replacement of conventional rigid grippers, and in recent years, they have been emerging exponentially in industries. Not only are these highly adaptable grippers capable of static grasping of an object, but also they can be utilized for performing object manipulation tasks. Plenty of contemporary

    更新日期:2020-11-25
  • Chitrakar: Robotic System for Drawing Jordan Curve of Facial Portrait
    arXiv.cs.RO Pub Date : 2020-11-21
    Aniruddha Singhal; Ayush Kumar; Shivam Thukral; Deepak Raina; Swagat Kumar

    This paper presents a robotic system (\textit{Chitrakar}) which autonomously converts any image of a human face to a recognizable non-self-intersecting loop (Jordan Curve) and draws it on any planar surface. The image is processed using Mask R-CNN for instance segmentation, Laplacian of Gaussian (LoG) for feature enhancement and intensity-based probabilistic stippling for the image to points conversion

    更新日期:2020-11-25
  • A Formal Approach to the Co-Design of Embodied Intelligence
    arXiv.cs.RO Pub Date : 2020-11-21
    Gioele Zardini; Dejan Milojevic; Andrea Censi; Emilio Frazzoli

    We consider the problem of formally co-designing embodied intelligence as a whole, from hardware components such as chassis and sensors to software modules such as control and perception pipelines. We propose a principled approach to formulate and solve complex embodied intelligence co-design problems, leveraging a monotone co-design theory. The methods we propose are intuitive and integrate heterogeneous

    更新日期:2020-11-25
  • Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments
    arXiv.cs.RO Pub Date : 2020-11-21
    Max Jwo Lem Lee; Li-Ta Hsu; Hoi-Fung Ng; Shang Lee

    Accurate smartphone-based outdoor localization system in deep urban canyons are increasingly needed for various IoT applications such as augmented reality, intelligent transportation, etc. The recently developed feature-based visual positioning system (VPS) by Google detects edges from smartphone images to match with pre-surveyed edges in their map database. As smart cities develop, the building information

    更新日期:2020-11-25
  • Object Rearrangement Using Learned Implicit Collision Functions
    arXiv.cs.RO Pub Date : 2020-11-21
    Michael Danielczuk; Arsalan Mousavian; Clemens Eppner; Dieter Fox

    Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of collision-free grasping or placement trajectories challenging. We propose a learned collision model that accepts scene and query object point clouds and

    更新日期:2020-11-25
  • Risk-Sensitive Motion Planning using Entropic Value-at-Risk
    arXiv.cs.RO Pub Date : 2020-11-23
    Anushri Dixit; Mohamadreza Ahmadi; Joel W. Burdick

    We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose the obstacle avoidance constraint in the MPC problem as a distributionally robust constraint with a KL divergence ambiguity set. This constraint is the dual representation of the Entropic Value-at-Risk (EVaR). Building upon this

    更新日期:2020-11-25
  • Semantic SLAM with Autonomous Object-Level Data Association
    arXiv.cs.RO Pub Date : 2020-11-20
    Zhentian Qian; Kartik Patath; Jie Fu; Jing Xiao

    It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and environments to be navigated. While semantic SLAM

    更新日期:2020-11-25
  • Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
    arXiv.cs.RO Pub Date : 2020-11-23
    Kunming Li; Stuart Eiffert; Mao Shan; Francisco Gomez-Donoso; Stewart Worrall; Eduardo Nebot

    Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated sampling of generative models. Additionally, most current predictive models are trained on datasets

    更新日期:2020-11-25
  • CORAL: Colored structural representation for bi-modal place recognition
    arXiv.cs.RO Pub Date : 2020-11-22
    Yiyuan Pan; Xuecheng Xu; Weijie Li; Yue Wang; Rong Xiong

    Place recognition is indispensable for drift-free localization system. Due to the variations of the environment, place recognition using single modality has limitations. In this paper, we propose a bi-modal place recognition method, which can extract compound global descriptor from the two modalities, vision and LiDAR. Specifically, we build elevation image generated from point cloud modality as a

    更新日期:2020-11-25
  • Deep Smartphone Sensors-WiFi Fusion for Indoor Positioning and Tracking
    arXiv.cs.RO Pub Date : 2020-11-21
    Leonid Antsfeld; Boris Chidlovskii; Emilio Sansano-Sansano

    We address the indoor localization problem, where the goal is to predict user's trajectory from the data collected by their smartphone, using inertial sensors such as accelerometer, gyroscope and magnetometer, as well as other environment and network sensors such as barometer and WiFi. Our system implements a deep learning based pedestrian dead reckoning (deep PDR) model that provides a high-rate estimation

    更新日期:2020-11-25
  • Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
    arXiv.cs.RO Pub Date : 2020-11-21
    Gioele Zardini; Andrea Censi; Emilio Frazzoli

    Designing cyber-physical systems is a complex task which requires insights at multiple abstraction levels. The choices of single components are deeply interconnected and need to be jointly studied. In this work, we consider the problem of co-designing the control algorithm as well as the platform around it. In particular, we leverage a monotone theory of co-design to formalize variations of the LQG

    更新日期:2020-11-25
  • Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
    arXiv.cs.RO Pub Date : 2020-11-21
    Andrew J. Taylor; Victor D. Dorobantu; Sarah Dean; Benjamin Recht; Yisong Yue; Aaron D. Ames

    Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model

    更新日期:2020-11-25
  • SymAR: Symmetry Abstractions and Refinement for Accelerating Scenarios with Neural Network Controllers Verification
    arXiv.cs.RO Pub Date : 2020-11-21
    Hussein Sibai; Yangge Li; Sayan Mitra

    We present a Symmetry-based abstraction refinement algorithm SymAR that is directed towards safety verification of large-scale scenarios with complex dynamical systems. The abstraction maps modes with symmetric dynamics to a single abstract mode and refinements recursively split the modes when safety checks fail. We show how symmetry abstractions can be applied effectively to closed-loop control systems

    更新日期:2020-11-25
  • A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
    arXiv.cs.RO Pub Date : 2020-11-20
    Di Feng; Ali Harakeh; Steven Waslander; Klaus Dietmayer

    Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there is no summary on uncertainty estimation in deep object detection, and existing methods are not only built with different network architectures and uncertainty

    更新日期:2020-11-25
  • Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System
    arXiv.cs.RO Pub Date : 2020-11-20
    Junhyeok Ahn; Luis Sentis

    Model-based reinforcement learning (MBRL) algorithms can attain significant sample efficiency but require an appropriate network structure to represent system dynamics. Current approaches include white-box modeling using analytic parameterizations and black-box modeling using deep neural networks. However, both can suffer from a bias-variance trade-off in the learning process, and neither provides

    更新日期:2020-11-25
  • Planning Folding Motion with Simulation in the Loop Using Laser Forming Origami and Thermal Behaviors as an Example
    arXiv.cs.RO Pub Date : 2020-11-20
    Yue Hao; Weilin Guan; Edwin A Peraza Hernandez; Jyh-Ming Lien

    Designing a robot or structure that can fold itself into a target shape is a process that involves challenges originated from multiple sources. For example, the designer of rigid self-folding robots must consider foldability from geometric and kinematic aspects to avoid self-intersection and undesired deformations. Recent works have shown success in estimating foldability of a design using robot motion

    更新日期:2020-11-23
  • Utilizing ROS 1 and the Turtlebot3 in a Multi-Robot System
    arXiv.cs.RO Pub Date : 2020-11-20
    Corey Williams; Adam Schroeder

    ROS (Robot Operating System) has become ubiquitous for testing new algorithms, alternative hardware configurations, and prototyping. By performing research with its modular framework, it can streamline sharing new work and integrations. However, it has many features and new terms that can take a considerable amount of time to learn for a new user. This paper will explore how to set up and configure

    更新日期:2020-11-23
  • Probabilistic Radio-Visual Active Sensing for Search and Tracking
    arXiv.cs.RO Pub Date : 2020-11-20
    L. Varotto; A. Cenedese; A. Cavallaro

    Active Search and Tracking for search and rescue missions or collaborative mobile robotics relies on the actuation of a sensing platform to detect and localize a target. In this paper we focus on visually detecting a radio-emitting target with an aerial robot equipped with a radio receiver and a camera. Visual-based tracking provides high accuracy, but the directionality of the sensing domain often

    更新日期:2020-11-23
  • Bridging Scene Understanding and Task Execution with Flexible Simulation Environments
    arXiv.cs.RO Pub Date : 2020-11-20
    Zachary Ravichandran; J. Daniel Griffith; Benjamin Smith; Costas Frost

    Significant progress has been made in scene understanding which seeks to build 3D, metric and object-oriented representations of the world. Concurrently, reinforcement learning has made impressive strides largely enabled by advances in simulation. Comparatively, there has been less focus in simulation for perception algorithms. Simulation is becoming increasingly vital as sophisticated perception approaches

    更新日期:2020-11-23
  • Accelerating Probabilistic Volumetric Mapping using Ray-Tracing Graphics Hardware
    arXiv.cs.RO Pub Date : 2020-11-20
    Heajung Min; Kyung Min Han; Young J. Kim

    Probabilistic volumetric mapping (PVM) represents a 3D environmental map for an autonomous robotic navigational task. A popular implementation such as Octomap is widely used in the robotics community for such a purpose. The Octomap relies on octree to represent a PVM and its main bottleneck lies in massive ray-shooting to determine the occupancy of the underlying volumetric voxel grids. In this paper

    更新日期:2020-11-23
  • Analytic Bipedal Walking with Fused Angles and Corrective Actions in the Tilt Phase Space
    arXiv.cs.RO Pub Date : 2020-11-20
    Philipp Allgeuer

    This thesis presents algorithms for the feedback-stabilised walking of bipedal humanoid robotic platforms, along with the underlying theoretical and sensorimotor frameworks required to achieve it. Bipedal walking is inherently complex and difficult to control due to the high level of nonlinearity and significant number of degrees of freedom of the concerned robots, the limited observability and controllability

    更新日期:2020-11-23
  • Simulation-based Testing for Early Safety-Validation of Robot Systems
    arXiv.cs.RO Pub Date : 2020-11-20
    Tom P. Huck; Christoph Ledermann; Torsten Kröger

    Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of robot systems, safety flaws often stay hidden, especially at early design stages, when a physical implementation is not yet available for testing. Simulation-based

    更新日期:2020-11-23
  • Learning Synthetic to Real Transfer for Localization and Navigational Tasks
    arXiv.cs.RO Pub Date : 2020-11-20
    Pietrantoni Maxime; Chidlovskii Boris; Silander Tomi

    Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done

    更新日期:2020-11-23
  • CLIPPER: A Graph-Theoretic Framework for Robust Data Association
    arXiv.cs.RO Pub Date : 2020-11-20
    Parker C. Lusk; Kaveh Fathian; Jonathan P. How

    We present CLIPPER (Consistent LInking, Pruning, and Pairwise Error Rectification), a framework for robust data association in the presence of noise and outliers. We formulate the problem in a graph-theoretic framework using the notion of geometric consistency. State-of-the-art techniques that use this framework utilize either combinatorial optimization techniques that do not scale well to large-sized

    更新日期:2020-11-23
  • FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
    arXiv.cs.RO Pub Date : 2020-11-20
    Tai Wang; Conghui He; Zhe Wang; Jianping Shi; Dahua Lin

    Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex

    更新日期:2020-11-23
  • MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty
    arXiv.cs.RO Pub Date : 2020-11-20
    Anubhav Guha; Anuradha Annaswamy

    Reinforcement learning (RL) algorithms have been successfully used to develop control policies for dynamical systems. For many such systems, these policies are trained in a simulated environment. Due to discrepancies between the simulated model and the true system dynamics, RL trained policies often fail to generalize and adapt appropriately when deployed in the real-world environment. Current research

    更新日期:2020-11-23
  • Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization
    arXiv.cs.RO Pub Date : 2020-11-19
    Yongqiang Huang; Juan Wilches; Yu Sun

    Pouring is one of the most commonly executed tasks in humans' daily lives, whose accuracy is affected by multiple factors, including the type of material to be poured and the geometry of the source and receiving containers. In this work, we propose a self-supervised learning approach that learns the pouring dynamics, pouring motion, and outcomes from unsupervised demonstrations for accurate pouring

    更新日期:2020-11-23
  • Lidar-based exploration and discretization for mobile robot planning
    arXiv.cs.RO Pub Date : 2020-11-19
    Yuxiao Chen; Andrew Singletary; Aaron D. Ames

    In robotic applications, the control, and actuation deal with a continuous description of the system and environment, while high-level planning usually works with a discrete description. This paper considers the problem of bridging the low-level control and high-level planning for robotic systems via sensor data. In particular, we propose a discretization algorithm that identifies free polytopes via

    更新日期:2020-11-23
  • Batteries, camera, action! Learning a semantic control space for expressive robot cinematography
    arXiv.cs.RO Pub Date : 2020-11-19
    Rogerio Bonatti; Arthur Bucker; Sebastian Scherer; Mustafa Mukadam; Jessica Hodgins

    Aerial vehicles are revolutionizing the way film-makers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work we develop a data-driven framework

    更新日期:2020-11-23
  • Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association
    arXiv.cs.RO Pub Date : 2020-11-06
    Yorai Shaoul; Katherine Liu; Kyel Ok; Nicholas Roy

    We propose a self-supervised method for incrementally refining visual descriptors to improve performance in the task of object-level visual data association. Our method optimizes deep descriptor generators online, by fine-tuning a widely available network pre-trained for image classification. We show that earlier layers in the network outperform later-stage layers for the data association task while

    更新日期:2020-11-23
  • Neural Stochastic Contraction Metrics for Learning-based Robust Control and Estimation
    arXiv.cs.RO Pub Date : 2020-11-06
    Hiroyasu Tsukamoto; Soon-Jo Chung; Jean-Jacques E. Slotine

    We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems. It uses a spectrally-normalized deep neural network to construct a contraction metric, sampled via simplified convex optimization in the stochastic setting. Spectral normalization constrains the state-derivatives of the metric

    更新日期:2020-11-23
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