Showing papers in "The International Journal of Robotics Research in 2017"
TL;DR: By frequently traversing the same route over the period of a year, this dataset enables research investigating long-term localization and mapping for autonomous vehicles in real-world, dynamic urban environments to be investigated.
Abstract: We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on av...
1,285 citations
TL;DR: A series of robotic experiments are reported that average a 93% end-to-end grasp success rate for novel objects presented in dense clutter, an improvement in grasp detection performance.
Abstract: Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose. The underlying i...
421 citations
TL;DR: Experimental results show that robust localization with high accuracy can be achieved with this filter-based framework, and there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame.
Abstract: This paper presents a visual-inertial odometry framework that tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter. By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of noncorner-shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach, which exhibits a high level of robustness with respect to low ...
385 citations
TL;DR: In this article, the authors proposed a dynamic and active-pixel vision sensor DAVIS, which incorporated a conventional global-shutter camera and an event-based sensor in the same pixel array.
Abstract: New vision sensors, such as the dynamic and active-pixel vision sensor DAVIS, incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have ...
370 citations
TL;DR: By explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances, and constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real time in environments with complex geometric constraints.
Abstract: We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we...
305 citations
TL;DR: An image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research, is presented.
Abstract: In this paper, we present an image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research. For ea...
282 citations
TL;DR: A constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional orientation, to avoid collisions with static and moving obstacles, and to make progress towards their goal.
Abstract: We present a constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional o...
189 citations
TL;DR: This paper presents a large-scale agricultural robot dataset for plant classification as well as localization and mapping that covers the relevant growth stages of plants for robotic intervention and weed control on a sugar beet farm near Bonn in Germany.
Abstract: There is an increasing interest in agricultural robotics and precision farming. In such domains, relevant datasets are often hard to obtain, as dedicated fields need to be maintained and the timing...
185 citations
TL;DR: The control framework is shown to provide stable bounding in the hardware, at speeds of up to 6.4 m/s and with a minimum total cost of transport of 0.47, unprecedented accomplishments in terms of efficiency and speed in untethered experimental quadruped machines.
Abstract: This paper presents the design and implementation of a bounding controller for the MIT Cheetah 2 and its experimental results. The paper introduces the architecture of the controller along with the...
169 citations
TL;DR: The results show that the exoskeleton exhibits good kinematic compatibility to the human body with a wide range of motion and performs task-space force and impedance control behaviors reliably.
Abstract: We present an upper-body exoskeleton for rehabilitation, called Harmony, that provides natural coordinated motions on the shoulder with a wide range of motion, and force and impedance controllability. The exoskeleton consists of an anatomical shoulder mechanism with five active degrees of freedom, and one degree of freedom elbow and wrist mechanisms powered by series elastic actuators. The dynamic model of the exoskeleton is formulated using a recursive Newton-Euler algorithm with spatial dynamics representation. A baseline control algorithm is developed to achieve dynamic transparency and scapulohumeral rhythm assistance, and the coupled stability of the robot-human system at the baseline control is investigated. Experiments were conducted to evaluate the kinematic and dynamic characteristics of the exoskeleton. The results show that the exoskeleton exhibits good kinematic compatibility to the human body with a wide range of motion and performs task-space force and impedance control behaviors reliably.
167 citations
TL;DR: This paper presents a generic probabilistic method for localizing an autonomous vehicle equipped with a threeD LIDAR scanner that is robust through heavy snowfall and roadway repavements and rasterize to facilitate fast and exact multiresolution inference.
Abstract: This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging LIDAR scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint e.g. puddles and snowdrifts, poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional 3D LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the z -height and reflectivity distribution of the environment-which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.
TL;DR: The Bounded-Memory Adaptation Model is proposed, which is a probabilistic finite-state controller that captures human adaptive behaviors under a bounded-memory assumption, enabling the robot to guide adaptable participants towards a better way of completing the task.
Abstract: Adaptation is critical for effective team collaboration. This paper introduces a computational formalism for mutual adaptation between a robot and a human in collaborative tasks. We propose the Bou...
TL;DR: It is demonstrated that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework.
Abstract: We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of the pipeline, exploits a large number of data samples, and can be framed additionally to refine handcrafted cost maps produced based on manual hand-engineered features. To achieve this, we introduce a maximum-entropy-based, non-linear inverse reinforcement learning IRL framework which exploits the capacity of fully convolutional neural networks FCNs to represent the cost model underlying driving behaviours. The application of a high-capacity, deep, parametric approach successfully scales to more complex environments and driving behaviours, while at deployment being run-time independent of training dataset size. After benchmarking against state-of-the-art IRL approaches, we focus on demonstrating scalability and performance on an ambitious dataset collected over the course of 1 year including more than 25,000 demonstration trajectories extracted from over 120 km of urban driving. We evaluate the resulting cost representations by showing the advantages over a carefully, manually designed cost map and furthermore demonstrate its robustness towards systematic errors by learning accurate representations even in the presence of calibration perturbations. Importantly, we demonstrate that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework.
TL;DR: This work conducted a series of human subject experiments to investigate the ways in which human factors influence the design of computational techniques, and provides design guidelines for the development of intelligent collaborative robots based on the results.
Abstract: Advancements in robotic technology are making it increasingly possible to integrate robots into the human workspace in order to improve productivity and decrease worker strain resulting from the pe...
TL;DR: The resulting model formulation captures the magnetically coupled catheter behavior and provides numerical solutions for rod equilibrium configurations in real-time and is general, covering cases with different catheter geometries, multiple magnetic components, and various boundary constraints.
Abstract: In this paper we apply Cosserat rod theory to catheters with permanent magnetic components that are subject to spatially varying magnetic fields. The resulting model formulation captures the magnet...
TL;DR: SimGrasp, a flexible dynamic hand simulator, enables parametric studies of the hand for acquisition and pull-out tests with varying transmission spring rates, and this work takes advantage of achieving different stiffnesses by reversing the direction of tendon windup using a torsional spring-loaded winch.
Abstract: Underactuated, compliant, tendon-driven robotic hands are suited for deep-sea exploration. The robust Ocean One hand design utilizes elastic finger joints and a spring transmission to achieve a var...
TL;DR: This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes, ideal to evaluate and benchmark appearance-based localization, monocular visual odometry, simultaneous localization and mapping, and online three-dimensional reconstruction algorithms for micro aerial vehicles in urban environments.
Abstract: This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5–15 m above the ground). The 2...
TL;DR: Transition State Clustering (TSC) models demonstrations as noisy realizations of a switched linear dynamical system, and learns spatially and temporally consistent transition events across demonstrations, and uses a hierarchical Dirichlet Process Gaussian Mixture Model to avoid having to select the number of segments a priori.
Abstract: A large and growing corpus of synchronized kinematic and video recordings of robot-assisted surgery has the potential to facilitate training and subtask automation. One of the challenges in segmenting such multi-modal trajectories is that demonstrations vary spatially, temporally, and contain random noise and loops (repetition until achieving the desired result). Segments of task trajectories are often less complex, less variable, and allow for easier detection of outliers. As manual segmentation can be tedious and error-prone, we propose a new segmentation method that combines hybrid dynamical systems theory and Bayesian non-parametric statistics to automatically segment demonstrations. Transition State Clustering (TSC) models demonstrations as noisy realizations of a switched linear dynamical system, and learns spatially and temporally consistent transition events across demonstrations. TSC uses a hierarchical Dirichlet Process Gaussian Mixture Model to avoid having to select the number of segments a priori. After a series of merging and pruning steps, the algorithm adaptively optimizes the number of segments. In a synthetic case study with two linear dynamical regimes, where demonstrations are corrupted with noise and temporal variations, TSC finds up to a 20% more accurate segmentation than GMM-based alternatives. On 67 recordings of surgical needle passing and suturing tasks from the JIGSAWS surgical training dataset [7], supplemented with manually annotated visual features, TSC finds 83% of needle passing segments and 73% of the suturing segments found by human experts. Qualitatively, TSC also identifies transitions overlooked by human annotators.
TL;DR: A novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots designed as a part of the Building-Wide Intelligence project at the University of Texas at Austin is introduced.
Abstract: Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand hum...
TL;DR: In this article, a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication, and the authors consider the following problem:
Abstract: We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. ...
TL;DR: The framework of Inverse KKT is proposed, which assumes that the demonstrations fulfill the Karush–Kuhn–Tucker conditions of an unknown underlying constrained optimization problem, and extracts parameters of this underlying problem.
Abstract: Inverse Optimal Control (IOC) assumes that demonstrations are the solution to an optimal control problem with unknown underlying costs, and extracts parameters of these underlying costs. We propose the framework of Inverse KKT, which assumes that the demonstrations fulfill the Karush–Kuhn–Tucker conditions of an unknown underlying constrained optimization problem, and extracts parameters of this underlying problem. Using this assumption, we can exploit the latter to extract the relevant task spaces and cost parameters from demonstrations of skills that involve contacts. For a typical linear parameterization of cost functions this reduces to a quadratic program, ensuring guaranteed and very efficient convergence, but we can deal also with arbitrary non-linear parameterizations of cost functions. The aim of our approach is to push learning from demonstration to more complex manipulation scenarios that include the interaction with objects and therefore the realization of contacts/constraints within the motion. We demonstrate the approach on tasks such as sliding a box and opening a door.
TL;DR: This paper introduces a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots and proves that the greedy algorithm is a 2-approximation for maximizing submodular tracking objective functions.
Abstract: Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we introduce a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the Probability Hypothesis Density filter to simultaneously estimate the number of targets and their positions. Next, we present a greedy algorithm for assigning trajectories to the robots to actively track the targets. We prove that the greedy algorithm is a 2-approximation for maximizing submodular tracking objective functions. We examine two such functions: the mutual information between the estimated target positions and future measurements from the robots, and the expected number of targets detected by the robot team. We provide extensive simulation evaluations using a real-world dataset.
TL;DR: A general framework for incremental maximum likelihood estimation called SLAM++ is introduced, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate.
Abstract: The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-kno...
TL;DR: The method is fundamentally different from approaches based on Dynamic Time Warping that must rely on a consistent stream of measurements at runtime, and can achieve phase estimation, action recognition and robot trajectory coordination using a single probabilistic representation.
Abstract: This paper proposes a method to achieve fast and fluid human–robot interaction by estimating the progress of the movement of the human. The method allows the progress, also referred to as the phase of the movement, to be estimated even when observations of the human are partial and occluded; a problem typically found when using motion capture systems in cluttered environments. By leveraging on the framework of Interaction Probabilistic Movement Primitives, phase estimation makes it possible to classify the human action, and to generate a corresponding robot trajectory before the human finishes his/her movement. The method is therefore suited for semi-autonomous robots acting as assistants and coworkers. Since observations may be sparse, our method is based on computing the probability of different phase candidates to find the phase that best aligns the Interaction Probabilistic Movement Primitives with the current observations. The method is fundamentally different from approaches based on Dynamic Time Warping that must rely on a consistent stream of measurements at runtime. The resulting framework can achieve phase estimation, action recognition and robot trajectory coordination using a single probabilistic representation. We evaluated the method using a seven-degree-of-freedom lightweight robot arm equipped with a five-finger hand in single and multi-task collaborative experiments. We compare the accuracy achieved by phase estimation with our previous method based on dynamic time warping.
TL;DR: A multi-modal control scheme for rehabilitation robotic exoskeletons that achieves the paradigm of “assist-as-needed” and also guarantees the safety of the human is presented.
Abstract: In the past few decades, a variety of rehabilitation robotic exoskeletons have been developed for patients with stroke and traumatic brain injury, which can assist therapists and potentially improv...
TL;DR: In this article, the authors formally verify corresponding controllers and provide rigorous safety proofs justifying why the robots can never collide with the obstacle in the respective physical model, which depends on the exact formulation of the safety objective, as well as the physical capabilities and limitations of the robot and the obstacles.
Abstract: This article answers fundamental safety questions for ground robot navigation: under which circumstances does which control decision make a ground robot safely avoid obstacles? Unsurprisingly, the answer depends on the exact formulation of the safety objective, as well as the physical capabilities and limitations of the robot and the obstacles. Because uncertainties about the exact future behavior of a robot’s environment make this a challenging problem, we formally verify corresponding controllers and provide rigorous safety proofs justifying why the robots can never collide with the obstacle in the respective physical model. To account for ground robots in which different physical phenomena are important, we analyze a series of increasingly strong properties of controllers for increasingly rich dynamics and identify the impact that the additional model parameters have on the required safety margins. We analyze and formally verify: (i) static safety, which ensures that no collisions can happen with stati...
TL;DR: The proposed dataset is particularly suited as a testbed for object and/or room categorization systems, but it can be also exploited for a variety of tasks, including robot localization, 3D map building, SLAM, and object segmentation.
Abstract: This paper presents the Robot-at-Home dataset Robot@Home, a collection of raw and processed sensory data from domestic settings aimed at serving as a benchmark for semantic mapping algorithms throu...
TL;DR: Algorithms for solving Dec-POSMDPs, which are more scalable than previous methods since they can incorporate closed-loop belief space macro-actions in planning and automatically constructed to produce robust solutions, are presented.
Abstract: This work focuses on solving general multi-robot planning problems in continuous spaces with partial observability given a high-level domain description. Decentralized Partially Observable Markov D...
TL;DR: A key challenge in robotic bipedal locomotion is the design of feedback controllers and periodic gaits that function well in the presence of modest terrain variation, without over-reliance on perception and a priori knowledge of the environment.
Abstract: A key challenge in robotic bipedal locomotion is the design of feedback controllers that function well in the presence of uncertainty, in both the robot and its environment. This paper addresses th...
TL;DR: The question of whether and when a robot should take initiative during joint human-robot task execution is addressed and a robotic system capable of autonomously performing table-top manipulation tasks while monitoring the environmental state is designed.
Abstract: The promise of robots assisting humans in everyday tasks has led to a variety of research questions and challenges in human-robot collaboration. Here, we address the question of whether and when a ...