IROS 2016 论文列表

In this paper we describe how we realized event-based signaling for large scale artificial robotic skin. We developed a new algorithm for the event generation on multi-modal skin cells. The skin cells have two modes, the conventional data sampling mode and the event mode. A comprehensive performance evaluation and comparison of these two modes is presented. We perform different experiments on our robot TOMM which has two UR5 robot arms, each covered with 260 multi-modal skin cells. Each skin cell samples 9 signals of 4 different modalities. Finally we derive models for extrapolating CPU usage and network traffic for larger numbers of skin cells and higher sample rates. The results show that the event-based system has superior performance and its performance edge increases with larger numbers of skin cells and higher sample rates. Experimental validation on our real robot system shows that in reactive control the event-based system reduces in comparison to the conventional system the packet rate by 48.2% and the CPU usage by 17.79%. We extrapolate the worst case for 5000 cells and show that the event-based system can at least reduce the packet rate by 21.2% and the CPU usage by 17.46%.

Autonomous driving technology has made significant advances in recent years. In order for self-driving cars to become practical, they are required to operate safely and reliably even under adverse driving conditions. However, most current autonomous driving cars have only been shown to be operational under amiable weather conditions, i.e., on sunny days on dry roads. In order to enable autonomous cars to handle adverse driving conditions such as rain and wet roads, the algorithm must be able to detect roads within a tolerable margin of error using sensors such as cameras and laser scanners. In this paper, we propose a sensor fusion algorithms that is able to operate under a variety of weather conditions, including rain. Our algorithm was validated when a strong shower occurred during the 2014 Hyundai Motor Company's Autonomous Car Competition. In this paper, we present the competition results that were collected on the same course on both sunny and rainy days. Based on the comparison, we propose the future directions to improve the autonomous driving capability under adverse environmental conditions.

In order to ensure safe operation, robots must be able to reliably detect behavior perturbations that result from unexpected physical interactions with their environment and human co-workers. While some robots provide firmware force sensors that generate rough force estimates, more accurate force measurements are usually achieved with dedicated force-torque sensors. However, such sensors are often heavy, expensive and require an additional power supply. In the case of lightweight manipulators, the already limited payload capabilities may be reduced in a significant way. This paper presents an experience-based approach for accurately estimating external forces being applied to a robot without the need for a force-torque sensor. Using Information Transfer, a subset of sensors relevant to the executed behavior are identified from a larger set of internal sensors. Models mapping robot sensor data to force-torque measurements are learned using a neural network. These models can be used to predict the magnitude and direction of perturbations from affordable, proprioceptive sensors only. Experiments with a UR5 robot show that our method yields force estimates with accuracy comparable to a dedicated force-torque sensor. Moreover, our method yields a substantial improvement in accuracy over force-torque values provided by the robot firmware.

Hardness sensing is a valuable capability for a robot touch sensor. We describe a novel method of hardness sensing that does not require accurate control of contact conditions. A GelSight sensor is a tactile sensor that provides high resolution tactile images, which enables a robot to infer object properties such as geometry and fine texture, as well as contact force and slip conditions. The sensor is pressed on silicone samples by a human or a robot and we measure the sample hardness only with data from the sensor, without a separate force sensor and without precise knowledge of the contact trajectory. We describe the features that show object hardness. For hemispherical objects, we develop a model to measure the sample hardness, and the estimation error is about 4% in the range of 8 Shore 00 to 45 Shore A. With this technology, a robot is able to more easily infer the hardness of the touched objects, thereby improving its object recognition as well as manipulation strategy.

Precisely estimating the pose of an agent in a global reference frame is a crucial goal that unlocks a multitude of robotic applications, including autonomous navigation and collaboration. In order to achieve this, current state-of-the-art localization approaches collect data provided by one or more agents and create a single, consistent localization map, maintained over time. However, with the introduction of lengthier sorties and the growing size of the environments, data transfers between the backend server where the global map is stored and the agents are becoming prohibitively large. While some existing methods partially address this issue by building compact summary maps, the data transfer from the agents to the backend can still easily become unmanageable. In this paper, we propose a method that is designed to reduce the amount of data that needs to be transferred from the agent to the backend, functioning in large-scale, multi-session mapping scenarios. Our approach is based upon a landmark selection method that exploits information coming from multiple, possibly weak and correlated, landmark utility predictors; fused using learned feature coefficients. Such a selection yields a drastic reduction in data transfer while maintaining localization performance and the ability to efficiently summarize environments over time. We evaluate our approach on a data set that was autonomously collected in a dynamic indoor environment over a period of several months.

Recently, the need for safe human-robot-interaction has become increasingly important, and with it the requirement to reliably detect persons in the workspace of a robot. Capacitive sensors mounted to the robot structure can be used to measure the presence of conductive objects and, hence, allow the detection of persons. However, various objects in the workspace can influence capacitive sensor measurements. Thus, we propose to record an environment model containing the expected sensor values for relevant robot poses. Using this model, distance estimation and real-time reaction can be performed even in the presence of additional conductive objects in the workspace. A demonstration of our approach was shown at the Hannover Messe 2015.

In this paper we present an interactive system to enhance bilateral teleoperation through online virtual fixtures generation and task switching. This is achieved using a stereo camera system which provides accurate information of the surrounding environment of the robot and of the tasks that have to be performed in it. The use of the proposed approach aims at improving the performances of bilateral teleoperation systems by reducing the human operator workload and increasing both the implementation and the execution efficiency. In fact, using our method virtual guidances do not need to be programmed a priori but they can be instead automatically generated and updated making the system suitable for unstructured environments. We strengthen the proposed method using passivity control in order to safely switch between different tasks while teleoperating under active constraints. A series of experiments emulating real industrial scenarios are used to show that the switch between multiple tasks can be passively and safely achieved and handled by the system.

In this paper we propose a novel technique for detecting loop closures on a trajectory by matching sequences of images instead of single instances. We build upon well established techniques for creating a bag of visual words with a tree structure and we introduce a significant novelty by extending these notions to describe the visual information of entire regions using Visual-Word-Vectors. The fact that the proposed approach does not rely on a single image to recognize a site allows for a more robust place recognition, and consequently loop closure detection, while reduces the computational complexity for long trajectory cases. We present evaluation results for multiple publicly available indoor and outdoor datasets using Precision-Recall curves, which reveal that our method outperforms other state of the art algorithms.

In this work, we propose a Frequency-based Action Descriptor (FADE) to represent human actions. In robotics, with the development of Programming by Demonstration (PbD) methods, representing and recognizing large sets of actions has become crucial to build autonomous systems that learn from humans. The FADE descriptor leverages Fast Fourier Transform (FFT) for action representation and is combined with the Manhattan distance for measuring similarities between actions. It is characterized by a low time and space complexity and is particularly suitable for classification of human actions. For clustering problems, we propose a modified version of FADE, called Uncompressed-FADE (U-FADE), which performs well in combination with Spectral Clustering algorithms at the price of a reduced compression. We compare FADE with action descriptors based on Singular Value Decomposition (SVD) and Hidden Markov Models (HMM) on the entire HDM05 motion capture database. Despite the high dimensionality of the problem, we obtained on the entire database a promising recognition rate of 78% combining FADE with a simple 1-NN classification algorithm. Furthermore, we achieved a rate of 98% on a small action set and 88% on a medium action set.

Energy consumption represents one of the most basic constraints for mobile robot autonomy. We propose a new framework to predict energy consumption using information extracted from publicly available maps. This method avoids having to model internal robot configurations, which are often unavailable, while still providing invaluable predictions for both explored and unexplored trajectories. Our approach uses a heteroscedastic Gaussian Process to model the power consumption, which explicitly accounts for variance due to exogenous latent factors such as traffic and weather conditions. We evaluate our framework on 30km of data collected from a city centre environment with a mobile robot travelling on pedestrian walkways. Results across five different test routes show an average difference between predicted and measured power consumption of 3.3%, leading to an average error of 6.6% on predictions of energy consumption. The distinct advantage of our model is our ability to predict measurement variance. The variance predictions improved by 84.3% over a benchmark.

This paper proposes a robotics therapeutic massage evaluation system using electroencephalogram (EEG) signal analysis approach. An anthropomorphic dual arm robot is developed in this work for massage application. Compare to other massage systems, our dual arm robot can provides diversified massage techniques through the impedance control of both Cartesian space and joint space. In order to evaluate the effectiveness of robotic therapeutic massage, the experiments are conducted by recording EEG signals of subject before and after robotic massage. Independent Components Analysis (ICA) is used to filter out artifacts of EEG signals. After signal processing, the power of delta, alpha and beta rhythms are analyzed. The experimental results show an increase in delta power and a decrease in alpha power which represents the relaxation response of subjects. These results give the scientific proof of the effectiveness of robotic therapeutic massage.

The increasing use of Variable Stiffness Actuators (VSAs) in robotic joints is helping robots to meet the demands of human-robot interaction, requiring high safety and adaptability. The key feature of a VSA is the ability to exploit internal elastic elements to obtain a variable output stiffness. These allow the joints to store mechanical energy supplied through interaction with the environment and make the system more robust, efficient, and safe. This paper discusses the design of leaf springs for a sub-class of VSAs that use variable lever arm ratios as means to change their output stiffness. Given the trade-off between compactness and the maximum energy storage capacity, the internal springs' dimensions and material choice are assessed through a theoretical analysis and practical experiments.

We address the problem of ego-noise reduction, i.e., suppressing the noise a robot causes by its own motions. Such noise degrades the recorded microphone signal massively such that the robot's auditory capabilities suffer. To suppress it, it is intuitive to use also motor data, since it provides additional information about the robot's joints and thereby the noise sources. We propose to fuse motor data to a recently proposed multichannel dictionary algorithm for ego-noise reduction. At training, a dictionary is learned that captures spatial and spectral characteristics of ego-noise. At testing, nonlinear classifiers are used to efficiently associate the current robot's motor state to relevant sets of entries in the learned dictionary. By this, computational load is reduced by one third in typical scenarios while achieving at least the same noise reduction performance. Moreover, we propose to train dictionaries on different microphone array geometries and use them for ego-noise reduction while the head to which the microphones are mounted is moving. In such scenarios, the motor guided approach results in significantly better performance values.

This paper reports on an active simultaneous localization and mapping (SLAM) framework that leverages the Bayes tree data structure for efficient planning. Evaluating information-theoretic objective functions in the context of active SLAM is a very expensive process that requires significant computational overhead. The contributions of this work involve exploiting the structure of the planning problem integrated with SLAM via the Bayes tree graphical model. Specifically, we propose a constrained variable ordering and subtree caching scheme that reduce computational complexity by eliminating redundant computations between candidate actions that are similar. We also propose an active SLAM framework that utilizes these concepts, and demonstrate the benefits of the approach with an underwater robot performing visual SLAM in a hybrid simulation environment.

In this work we study the efficient search of objects in domestic environments, using probabilistic logic to represent uncertainty about object location and partially observable Markov decision processes (POMDP) for the decision-making process regarding the movements to be carried out by the robot to improve its belief about the object locations. We propose the use of a semantic map that stores information about the knowledge in the system and updates it, by an inference process, with sensor information received from the object recognition module. However, semantic maps are not capable of actively search for more information in the environment. For that reason a decision-making module, based on a POMDP framework, is integrated in the system. Several experiments were made in a realistic apartment test bed using every day objects and a mobile robot, showing that this hybrid solution makes the search process more efficient.

Visual SLAM is an area of vivid research and bears countless applications for moving robots. In particular, micro aerial vehicles benefit from visual sensors due to their low weight. Their motion is, however, often faster and more complex than that of ground-based robots which is why systems with multiple cameras are currently evaluated and deployed. This, in turn, drives the computational demand for visual SLAM algorithms.

Keypoint features detection from measurements enables efficient localization and map estimation through the compact representation and recognition of locations. The keypoint detector FALKO has been proposed to detect stable points in laser scans for localization and mapping tasks. In this paper, we present novel loop closure methods based on FALKO keypoints and compare their performance in online localization and mapping problems. The pose graph formulation is adopted, where each pose is associated to a local map of keypoints extracted from the corresponding laser scan. Loops in the graph are detected by matching local maps in two steps. First, the candidate matching scans are selected by comparing the scan signatures obtained from the keypoints of each scan. Second, the transformation between two scans is obtained by pairing and aligning the respective keypoint sets. Experiments with standard benchmark datasets assess the performance of FALKO and of the proposed loop closure algorithms in both offline and online localization and map estimation.

Standing up after falling is an essential ability for humanoid robots in order to resume their tasks without help from humans. Although many humanoid robots, especially small-size humanoid robots, have their own stand-up motions, there has not been a generalized method to automatically learn flexible stand-up motions for humanoid robots which can be applied to various fallen positions. In this research, we propose a method for learning stand-up motions for humanoid robots using Q-learning making use of their bilateral symmetry. We implemented this method on DarwIn-OP humanoid robots and learned an optimal policy in simulation. We compared the resulting stand-up motion with manually designed stand-up motions and with stand-up motions learned without considering bilateral symmetry. Both in simulation and on the real robot, the new stand-up motion was successful in most trials while other motions took longer or were not as robust.

This paper addresses the problem of road scene segmentation in conventional RGB images by exploiting recent advances in semantic segmentation via convolutional neural networks (CNNs). Segmentation networks are very large and do not currently run at interactive frame rates. To make this technique applicable to robotics we propose several architecture refinements that provide the best trade-off between segmentation quality and runtime. This is achieved by a new mapping between classes and filters at the expansion side of the network. The network is trained end-to-end and yields precise road/lane predictions at the original input resolution in roughly 50ms. Compared to the state of the art, the network achieves top accuracies on the KITTI dataset for road and lane segmentation while providing a 20× speed-up. We demonstrate that the improved efficiency is not due to the road segmentation task. Also on segmentation datasets with larger scene complexity, the accuracy does not suffer from the large speed-up.