Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology（哈利法大学自主机器人系统中心（KUCARS），哈利法科技大学）；Kingston University（金斯顿大学）；Purdue University（普渡大学）
Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based camera sets a limitation on continuous visual feedback due to their low sampling rate and redundant data in real-time image processing, especially in the case of high-speed tasks. Event cameras give human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution ($1μs$) with low latency and wide dynamic range.
In this paper, we present a visual servoing method using an event camera and a switching control strategy to explore, reach and grasp to achieve a manipulation task. We devise three surface layers of active events to directly process stream of events from relative motion. A purely event based approach is adopted to extract corner features, localize them robustly using heat maps and generate virtual features for tracking and alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in spatio-temporal space. The controller switches its strategy based on the sequence of operation to establish a stable grasp. The event based visual servoing (EVBS) method is validated experimentally using a commercial robot manipulator in an eye-in-hand configuration. Experiments prove the effectiveness of the EBVS method to track and grasp objects of different shapes without the need for re-tuning.
University of Edinburgh（爱丁堡大学）；University of Alicante（阿利坎特大学）
Visual servoing is a well-known task in robotics. However, there are still challenges when multiple visual sources are combined to accurately guide the robot or occlusions appear. In this paper we present a novel visual servoing approach using hybrid multi-camera input data to lead a robot arm accurately to dynamically moving target points in the presence of partial occlusions. The approach uses four RGBD sensors as Eye-to-Hand (EtoH) visual input, and an arm-mounted stereo camera as Eye-in-Hand (EinH). A Master supervisor task selects between using the EtoH or the EinH, depending on the dista…
The image-based visual servoing without models of system is challenging since it is hard to fetch an accurate estimation of hand-eye relationship via merely visual measurement. Whereas, the accuracy of estimated hand-eye relationship expressed in local linear format with Jacobian matrix is important to whole system's performance. In this article, we proposed a finite-time controller as well as a Jacobian matrix estimator in a combination of online and offline way. The local linear formulation is formulated first. Then, we use a combination of online and offline method to boost the estimation o…
Institute for Infocomm Research (IR), A*STAR（A*STAR资讯通信研究院（IR））；Institute for High Performance Computing (IHPC), A*STAR（A*STAR高性能计算研究院（IHPC））
We present KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system. We train the deep neural network only in the simulated environment; and the trained model could be directly used for real-world visual servoing tasks. KOVIS consists of two networks. The first keypoint network learns the keypoint representation from the image using with an autoencoder. Then the visual servoing network learns the motion based on keypoints extracted from the camera image. The two networks are trained end-to-end in the simula…
Vision-based control provides a significant potential for the end-point positioning of continuum robots under physical sensing limitations. Traditional visual servoing requires feature extraction and tracking followed by full or partial pose estimation, limiting the controller's efficiency. We hypothesize that employing deep learning models and implementing direct visual servoing can effectively resolve the issue by eliminating such intermediate steps, enabling control of a continuum robot without requiring an exact system model. This paper presents the control of a single-section tendon-drive…
An Intelligent Water-Saving Irrigation System Based on Multi-Sensor Fusion and Visual Servoing Control
Xi'an Jiaotong University Faculty of Microelectronics Science（西安交通大学微电子科学与工程学院）；Xi'an Jiaotong University School of Electrical Engineering（西安交通大学电气工程学院）
Nowadays, with the continuous expansion of application scenarios of robotic arms, there are more and more scenarios where nonspecialist come into contact with robotic arms. However, in terms of robotic arm visual servoing, traditional Position-based Visual Servoing (PBVS) requires a lot of calibration work, which is challenging for the nonspecialist to cope with. To cope with this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people from tedious calibration work. This work applied a model-free adaptive control (MFAC) method which means that the parameters of controller are …
Luddy School of Informatics, Computing, and Engineering, Indiana University（印第安纳大学卢迪信息学、计算与工程学院）
In this paper, we open up new avenues for visual servoing systems built upon the Path Integral (PI) optimal control theory, in which the non-linear partial differential equation (PDE) can be transformed into an expectation over all possible trajectories using the Feynman-Kac (FK) lemma. More precisely, we propose an MPPI-VS control strategy, a real-time and inversion-free control strategy on the basis of sampling-based model predictive control (namely, Model Predictive Path Integral (MPPI) control) algorithm, for both image-based, 3D point, and position-based visual servoing techniques, taking…
Prosthetic Hand Manipulation System Based on EMG and Eye Tracking Powered by the Neuromorphic Processor AltAi
Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）
We evaluate different state representation methods in robot hand-eye coordination learning on different aspects. Regarding state dimension reduction: we evaluates how these state representation methods capture relevant task information and how much compactness should a state representation be. Regarding controllability: experiments are designed to use different state representation methods in a traditional visual servoing controller and a REINFORCE controller. We analyze the challenges arisen from the representation itself other than from control algorithms. Regarding embodiment problem in LfD…
We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide ta…
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real …
Khalifa University of Science and Technology（哈利法科技大学）；Strata Manufacturing PJSC（斯特拉塔制造公司）
The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide d…
Queensland University of Technology（昆士兰科技大学）；LYRO Robotics（LYRO机器人公司）
This paper introduces DGBench, a fully reproducible open-source testing system to enable benchmarking of dynamic grasping in environments with unpredictable relative motion between robot and object. We use the proposed benchmark to compare several visual perception arrangements. Traditional perception systems developed for static grasping are unable to provide feedback during the final phase of a grasp due to sensor minimum range, occlusion, and a limited field of view. A multi-camera eye-in-hand perception system is presented that has advantages over commonly used camera configurations. We qu…
Khalifa University（哈利法大学）；Khalifa University of Science and Technology（哈利法科学技术大学）；Strata Manufacturing PJSC（斯特拉制造有限公司）；Purdue University（普渡大学）；Kingston University（金斯顿大学）
Robotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. Compared with standard frame-based vision, neuromorphic vision has advantages of microsecond-level sampling rate and no motion blur. Building on that, the model-based and model-free approache…
We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work stems from the observation that humans perform eye-hand coordination tasks by using geometric primitives to define a task while a geometric control error drives the task through execution. We propose a graph based kernel regression method to directly infer the underlying association constraints from human demonstration video using Incremental Maximum Entropy Inverse Reinforcement Learning (InMaxEnt IRL). The learned …
AI-Enabled Image-Based Hybrid Vision/Force Control of Tendon-Driven Aerial Continuum Manipulators
Department of Mechanical, Industrial, and Mechatronics Engineering, Toronto Metropolitan University（机械、工业与机电工程系，多伦多 Metropolitan 大学）；Department of Mechanical, Industrial, and Aerospace Engineering (MIAE), Concordia University（机械、工业与航空航天工程系（MIAE），康科迪亚大学）
University of Maryland（马里兰大学）；Zhejiang University（浙江大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；Hong Kong University of Science and Technology（香港科技大学）
