IROS 2018 论文列表

Robotics applications in the raw materials sector are becoming increasingly common due to their many perceived benefits. In mining, the extended use of robotics is especially seen in the exploration and exploitation phases, where mineral resources are discovered, extracted and processed. The use of robotics in the mining industry started in the 60s and today it is seen in the automation of material transport or in robotic digging and loading. Potential benefits include improved productivity, decreased production costs, better operational efficiency, increased safety, reduced waste and, ultimately, more value creation. The increasing amount of robotics used in the raw materials sector is coupled with a series of ethical and legal issues, regulatory challenges and policy requirements that affect both producers and end-users of robotic technologies. The benefits and challenges of robotics applications, often overlooked by the stakeholders, can hinder both their integration in the sector and the further development of mining activities, if not properly addressed.

Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024×1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.

We present a real-time, data-driven algorithm to enhance the social-invisibility of robots within crowds. Our approach is based on prior psychological research, which reveals that people notice and-importantly-react negatively to groups of social actors when they have high entitativity, moving in a tight group with similar appearances and trajectories. In order to evaluate that behavior, we performed a user study to develop navigational algorithms that minimize entitativity. This study establishes mapping between emotional reactions and multi-robot trajectories and appearances, and further generalizes the finding across various environmental conditions. We demonstrate the applicability of our entitativity modeling for trajectory computation for active surveillance and dynamic intervention in simulated robot-human interaction scenarios. Our approach empirically shows that various levels of entitative robots can be used to both avoid and influence pedestrians while not eliciting strong emotional reactions, giving multi-robot systems socially-invisibility.

Enter the RobotriX, an extremely photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems. The RobotriX consists of hyperrealistic indoor scenes which are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world. Photorealistic scenes and robots are rendered by Unreal Engine into a virtual reality headset which captures gaze so that a human operator can move the robot and use controllers for the robotic hands; scene information is dumped on a per-frame basis so that it can be reproduced offline using UnrealCV to generate raw data and ground truth labels. By taking this approach, we were able to generate a dataset of 38 semantic classes across 512 sequences totaling 8M stills recorded at +60 frames per second with full HD resolution. For each frame, RGB-D and 3D information is provided with full annotations in both spaces. Thanks to the high quality and quantity of both raw information and annotations, the RobotriX will serve as a new milestone for investigating 2D and 3D robotic vision tasks with large-scale data-driven techniques.

In this paper, we study the influence of a handshake in the human emotional bond to a robot. In particular, we evaluate the human willingness to help a robot whether the robot first introduces itself to the human with or without a handshake. In the tested paradigm the robot and the human have to perform a joint task, but at a certain stage, the robot needs help to navigate through an obstacle. Without requesting explicit help from the human, the robot performs some attempts to navigate through the obstacle, suggesting to the human that it requires help. In a study with 45 participants, we measure the human's perceptions of the social robot Vizzy, comparing the handshake vs non-handshake conditions. In addition, we evaluate the influence of a handshake in the pro-social behaviour of helping it and the willingness to help it in the future. The results show that a handshake increases the perception of Warmth, Animacy, Likeability, and the tendency to help the robot more, by removing the obstacle.

In times of more and more complex interaction techniques, we point out the powerfulness of colored light as a simple and cheap feedback mechanism. Since it is visible over a distance and does not interfere with other modalities, it is especially interesting for mobile robots. In an online survey, we asked 56 participants to choose the most appropriate colors for scenarios that were presented in the form of videos. In these scenarios a mobile robot accomplished tasks, in some with success, in others it failed because the task is not feasible, in others it stopped because it waited for help. We analyze in what way the color preferences differ between these three categories. The results show a connection between colors and meanings and that it depends on the participants' technical affinity, experience with robots and gender how clear the color preference is for a certain category. Finally, we found out that the participants' favorite color is not related to color preferences.

This work presents the concept of a robotic gripper designed for the disassembly of electromechanical devices that comprises several innovative ideas. Novel concepts include the ability to interchange built-in tools without the need to grasp them, the ability to reposition grasped objects in-hand, the capability of performing classic dual arm manipulation within the gripper and the utilization of classic industrial robotic arms kinematics within a robotic gripper. We analyze state of the art grippers and robotic hands designed for dexterous in-hand manipulation and extract common characteristics and weak points. The presented concept is obtained from the task requirements for disassembly of electromechanical devices and it is then evaluated for general purpose grasping, in-hand manipulation and operations with tools. We further present the CAD design for a first prototype.

The development and control of prosthetic hands is an active research area and recently progress in mechatronics, sensor integration and innovative control has been made. However, integration of different components into a prosthetic hand remains challenging due to space constraints, the requirements regarding holistic integration and the need for a user interface. In this paper, we present the KIT prosthetic hand, a novel five-finger 3D printed hand prosthesis, with its underactuated mechanism, sensors and embedded control system. The hand mechanics is based on the underactuated TUAT/Karlsruhe mechanism with two motors actuating 10 degrees of freedom. The mechanism has been realized in 3D printing technologies to facilitate a personalization of the prosthetic hand in terms of size and kinematic parameters. The prosthesis has been designed as a 50th percentile male hand. It integrates an advanced embedded system as well as an RGB camera in the base of the palm and a colour display in the back of the hand. Experiments indicate a finger tip force of 7.48 N to 11.82 N, a hook grasp force of 120 N and a hand closing time of ~ 1.3 s.

In this paper, we present a model set for designing human-robot collaboration (HRC) experiments. It targets a common scenario in HRC, which is the collaborative assembly of furniture, and it consists of a combination of standard components and custom designs. With this work, we aim at reducing the amount of work required to set up and reproduce HRC experiments, and we provide a unified framework to facilitate the comparison and integration of contributions to the field. The model set is designed to be modular, extendable, and easy to distribute. Importantly, it covers the majority of relevant research in HRC, and it allows tuning of a number of experimental variables that are particularly valuable to the field. Additionally, we provide a set of software libraries for perception, control and interaction, with the goal of encouraging other researchers to proactively contribute to our work.

“Autonomous robotics” promise significant improvements across a host of different complex systems, which will need to be managed within regulatory frameworks to promote, at a minimum, device safety. Contrary to how they are often portrayed, however, these systems do not necessarily require fundamentally new approaches to engineering or regulatory challenges, i.e., the development of a novel “autonomy framework” applicable to different types of devices. Rather, because autonomous systems generally represent a progressive improvement of existing complex systems, preexisting regulatory scheme offer the best guidance for considering future regulation of autonomous elements. Moreover, the regulatory landscape differs considerably based on the type of device at issue (e.g., consumer electronics vis-á-vis medical devices). This paper argues that users and regulators must consider future autonomy regulations within the specific framework those devices currently inhabit, rather than focusing on a novel set of rules divorced from the preexisting context.

Swing Leg Retraction (SLR) is observed in human walking and running. Previous studies have concluded that SLR improves the stability and robustness of biped walking. But this conclusion was based on analysis of robot models that can only walk at a very small range of step-lengths and slow or fixed speeds. By contrast, humans can walk with a large range of speeds and step-lengths. Moreover, human walking patterns have a special feature that has not been considered in the previous studies on SLR effects: At a given walking speed, v, humans prefer a step-length, s, which satisfies the power law, s-vβ. Therefore, previous studies on SLR can't tell us whether their conclusion will still hold in the full range of human walking patterns (i.e., various walking speeds and step-lengths). This is the question we want to answer in this paper. In this study, using a simple biped model, we studied how the SLR affects the walking stability in the full range of human walking speeds/step-lengths. Preliminary analysis of both models suggests the same conclusion: (1) SLR improves the stability more evidently in human-preferred walking patterns than in other walking patterns. (2) In walking patterns that are very unlike human-preferred ones, the SLR improves the stability very little, or even deteriorates it drastically. Therefore, the new finding of our study is that how the SLR affects the biped walking stability depends on the walking speed and step-length. SLR does not always improve the stability of biped walking.

Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but monocular localization demonstrations have been confined to plain roads. We demonstrate - to the best of our knowledge - the first results for monocular object localization and shape estimation on surfaces that are non-coplanar with the moving ego vehicle mounted with a monocular camera. We approximate road surfaces by local planar patches and use semantic cues from vehicles in the scene to initialize a local bundle-adjustment like procedure that simultaneously estimates the 3D pose and shape of the vehicles, and the orientation of the local ground plane on which the vehicle stands. We also demonstrate that our approach transfers from synthetic to real data, without any hyperparameter-/fine-tuning. We evaluate the proposed approach on the KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations. The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.

The paper presents the development of a novel deformable quad-rotor enabled aerial gripper. The mechanism of our deformable quad-rotor is based on simultaneous expansion or contraction of the quad-rotor body, which is generated by controlling a rigid elements based morphing structure (REMS). Such deformation results in a highly deformable quad-rotor that can not only perform morphological adaptation in response to environmental changes and obstacles, but also improve the flight performance by contracting to facilitate the agility/maneuverability or by expanding to enhance the stability. Meanwhile, inspired by the wasp grasping behavior, such controllable expansion and contraction from the REMS ingeniously enable a new function of aerial gripper. In this paper, we start to detail the mechanism and design of the REMS based deformable quad-rotor, then present the quad-rotor deformation enabled aerial gripper design, its dynamics modeling, the grasping function and analysis. The simulation was conducted in order to graphically show the elicited aerodynamic flow situation during expansion or contraction of the quad-rotor with and without carrying payload. Experiments were further implemented to validate the grasping function of the gripper and the flight performance of the quad-rotor. Finally, two case studies on the new aerial gripper were performed. All results demonstrate the excellent performance of the deformable quad-rotor enabled aerial gripper, that is, it has the advantages of both flight maneuverability and grasping capability during performing tasks.

In this paper, we introduce a set of guidelines for the design of grippers suitable for a safe human robot/interaction in cooperative tasks. Modularity, adaptability, robustness, intuitive control, limited weight are some of the key elements that could allow to effectively spread these devices in industrial and service applications. Following such guidelines, we present the prototype of the Co-Gripper: a robotic device for cooperative manipulation tasks with humans. The gripper is composed of two pairs of fingers, actuated with two motors, that can be controlled in a coordinated way or independently. Each finger has a modular underactuated structure, composed of three phalanges connected by passive joints. The gripper is wireless, so it can be easily connected both to the robotic arms and on passive structures. We designed a wearable wireless control interface composed of a ring and a bracelet allowing a simple and intuitive activation of the gripper without limiting human operator's manipulation capabilities. We performed a set of tests to quantify gripper performance and to exploit its potentialities in human-robot cooperation tasks.

Performing skillfull manipulation is a very challenging task for robots. So far, even experts could barely program them to e.g. perform the well known peg-in-hole problem in the real world. Autonomously acquiring such skills, let alone generalizing them to new tasks, is still a major challenge. Typically, manipulation learning is approached with the help of large computation power, very long learning times, or possibly both. However, the performance achieved up to now is still far from human performance. We show the results of our new paradigm to robot manipulation. It bridges and unifies basic motor control, simple and complex manipulation strategies and high-level manipulation planning. The robots show autonomous skill learning, intra-class and inter-class generalization of insertion skills at human-level performance.

Recreational videography from small drones can capture bystanders who may be uncomfortable about appearing in those videos. Existing privacy filters, such as scrambling and hopping blur, address this issue through de-identification but generate temporal distortions that manifest themselves as flicker. To address this problem, we present a robust spatiotemporal hopping blur filter that protects privacy through de-identification of face regions. The proposed filter is meant for on-board installation and produces temporally smooth and pleasant videos. We apply hopping blur to protect each frame against identification attacks, and minimise artefacts and flicker introduced by the hopping blur. We evaluate the proposed filter against different identification attacks and by assessing the quality of the resulting videos using a subjective test and objective measures.

We teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth imaging at multiple viewpoints. In virtual reality, a human supervisor can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 100 000 example grasps of fish. Using this data set for training purposes, the network is able to guide a real robot and gripper to grasp real fish with good success rates. The newly proposed domain randomization approach constitutes the first step in how to efficiently perform robot imitation learning from a human supervisor in virtual reality in a way that transfers well to the real world.

Teaching a robot to predict and mimic how a human moves or acts in the near future by observing a series of historical human movements is a crucial first step in human-robot interaction and collaboration. In this paper, we instrument a robot with such a prediction ability by leveraging recent deep learning and computer vision techniques. First, our system takes images from the robot camera as input to produce the corresponding human skeleton based on real-time human pose estimation obtained with the OpenPose library. Then, conditioning on this historical sequence, the robot forecasts plausible motion through a motion predictor, generating a corresponding demonstration. Because of a lack of high-level fidelity validation, existing forecasting algorithms suffer from error accumulation and inaccurate prediction. Inspired by generative adversarial networks (GANs), we introduce a global discriminator that examines whether the predicted sequence is smooth and realistic. Our resulting motion GAN model achieves superior prediction performance to state-of-the-art approaches when evaluated on the standard H3.6M dataset. Based on this motion GAN model, the robot demonstrates its ability to replay the predicted motion in a human-like manner when interacting with a person.

A novel brachiating robot design is presented for the purpose of traversing elevated wire networks. The robot is capable of moving along a single wire and between parallel wires, thereby enabling traversal of a two-dimensional space. Several novel features distinguish this design compared to previous brachiating robots. These include the ability to transition to and from both “rope” and “ladder” brachiation modes through an integrated wrist, a locking hand design for minimal power consumption, and distributed electronics packages that communicate wirelessly. A payload mounting point is installed, offering space for a variety of remote sensing packages. Experimental results using a prototype robot demonstrate that the system can reliably brachiate along a single wire, and can also reliably perform a swing-up maneuver after failed swing attempts or when transitioning between the rope and ladder locomotion modes. Energy expenditure for a single swing is quantified using experimental data. Overall, the proposed robot design is shown to provide a promising platform for traversal of wire networks in two dimensions.