3P1-S03 ロボットと人による二人あやとりの実現(ロボットハンドの機構と把持戦略(1))

We have developed a sensor to detect position and posture of an object in a short-range, called a "proximity sensor". The proximity sensors are mounted on fingertips of a robot hand, and it is realized that the fingertip surface and an object surface face each other before contact in grasping process by the robot. We call such postural adjustment "preshaping". When grasping an object by a robot hand system with the combination of the proximity sensor and a visual sensor, the visual sensor has only to guide the robot hand so that the proximity sensor detects the object and pre-shaping is performed appropriately. In this paper, we investigate the information which the visual sensor should acquire to perform pre-shaping appropriately.

1P1-N-088 家庭用サービスロボット向けエンドエフェクタの開発 : 多指多関節ロボットハンド(ロボットハンドの機構と把握戦略2,生活を支援するロボメカ技術のメガインテグレーション)

This paper presents a back-drivable two-fingered four-degree of freedom hand that can generate a large grasping force of 120N. All fingers are 98 mm in length and the weight of the hand is 1.2kg. The hand can generate a large grasp force with a new force-magnification mechanism and all joints can perform a high speed motion of larger than 750deg/s with a high-speed driving mechanism. The force-magnification mechanism installed in our previous hands need a non-backdrivable element, which loses compliance. The new force-magnification mechanism is connected to the high-speed driving mechanism only when it is activated. Therefore the high-speed driving mechanism can be back driven maintaining high compliance.

One of the problems on robots that work at human enviroments is manipulation ability. In this research, we focus on manipulation of slippery objects. When robots has to manipulate oily dishes, for example, utilization of slip information is important. The purpose of this research is realizing robot manipulation of such objects by using slip and contact force information. This paper proposes a estimation method of falling time and planning method of trajectory by using slip information. We also performed preliminary experiments on the methods.

This paper presents the impedance control of force instuction -type This control suppresses the excessive power and vibration This is effective for a rigid body and a exible object

The Robot Grasping and Manipulation Competition, held during the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) in Daejeon, South Korea was sponsored by the IEEE Robotic and Automation Society (RAS) Technical Committee (TC) on Robotic Hands Grasping and Manipulation (RHGM) [1]. This competition was the first of a planned series of grasping and manipulation-themed events of increasing difficulty that are intended to spur technological developments and advance test methods and benchmarks so that they can be formalized for use by the community. The coupling of standardized performance testing with robot competitions will promote the use of unbiased evaluation methods to assess how well a robot system performs in a particular application space. A strategy is presented for a series of grasping and manipulation competitions that facilitate objective performance benchmarking of robotic assembly solutions. This strategy is based on test methods that can be used for more rigorous assessments and comparison of systems and components outside of the competition regime. While competitions have proven to be useful mechanisms for assessing the relative performance of robotic systems with measures of success, they often lack a methodical measurement science foundation. Consequently, scientifically sound and statistically significant metrics, measurement, and evaluation methods to quantify performance are missing. Using performance measurement methods in a condensed format will accommodate competition time limits while introducing the methods to the community as tools for benchmarking performance in the developmental and deployment phases of a robot system. The particular evaluation methods presented here are focused on the mechanical assembly process, an application space that is expected to accelerate with the new robot technologies coming to market.

In this article we describe a general-purpose robotic grasping system for use in unstructured environments. Using computer vision and a compact set of heuristics, the system automatically generates the robot arm and hand motions reqrsired for grasping an unmodeled object. The utility of such a system is most evident in environ ments where the robot will have to grasp and manipulate a variety of unknown objects, but many of these manipu lation tasks may be relatively simple. Examples of such domains are planetary exploration and astronaut assis tance, undersea salvage and rescue, and nuclear waste site clean-up. This work implements a two-stage model of grasping: stage one is an orientation of the hand and wrist and a ballistic reach toward the object; stage two is hand preshaping and adjustment. Visual features are first extracted from the unmodeled object. These features and their relations are used by an expert system to generate a set of valid reach/grasps for the object. These grasps are then used in driving the robot hand and arm to bring the fingers into contact with the object in the desired configu ration. Experimental results are presented to illustrate the functioning of the system.

Robot grasping under the instruction of natural language has attracted increasing attention in various applications for its advantages in enabling natural and smooth human-robot interaction. At present, mainstream algorithms mainly solve problems of utilizing simple natural language instructions to guide the robot arm to perform some specific grasping. However, for two natural language instructions with different temporal logic and the same semantics, it is usually difficult for the robot to achieve semantic disambiguation, which further leads to the failure of the grasping task. In order to address this problem, we propose a new natural language instruction disambiguation method for robot grasping by combining sentence vector similarity calculation model and sentence temporal logic model. Firstly, the word vector is obtained through the Skip-gram model in Word2vec and a sentence vector is constructed. The semantic similarity of the sentence is then calculated by using the proposed cost function. Based on the semantic similarity of the sentence, the correct temporal logic form of the sentence is then extracted according to the temporal adverbial priority to further guide the grabbing process of the robot arm. The experimental results show that our method can successfully realize the semantic disambiguation for natural language instructions with different temporal logics and the same semantics, and further guide the robot arm to complete more complicated tasks than previous tasks.

In this paper, we propose a Learning from Demonstration approach for robotic grasping with compliant arms. The compliance in the robot arm for safety often causes a problem in grasping. In our approach, we construct a recurrent neural network, given the estimation of the target object position and random initial joint angles of the robot arm, that can produce the whole trajectories for grasping the target object. In order to generate smooth and stable trajectories and to decrease the number of human demonstrations, we propose a data augmentation method to increase the training data and utilize the trajectory planning technique using cubic splines for smooth and stable trajectories. Specifically, the two arms of the robot are trained respectively, and a support vector machine is used to decide which arm needs to be used for grasping the target object. The evaluation results show that our recurrent model not only has a good prediction for the final joint configurations, but also generates smooth and stable trajectory. Moreover, the model is robust to the changes in the initial joint state which means that even though the initial joint configuration is affected by disturbances, the model can still generate trajectories leading to the final joint configurations for grasping the object. Finally, we tested the proposed learning method on the Pepper robot which can successfully grasp randomly placed object on the workbench. Compared to traditional methods which need to avoid singular configurations as well as to secure accurate localization, our method turns out to be robust and efficient and can be applied to cluttered environment.

This paper presents an integrated system that combines learning, a natural-language interface, and robotic grasping to enable the transfer of grasping skills from nontechnical users to robots. The system consists of two parts: a natural-language interface for grasping commands and a learning system. This paper focuses on the learning system and testing of the entire system in a small usability study. The learning system presented consists of two phases. In the first phase, the system learns to predict the next command, which the user is planning to issue based on command sequences recorded during previous grasping sessions. In the second phase, the system predicts the user's current state and moves the robot's gripper to the intended target endpoint to attempt to grasp the object. Using eight nontechnical users and a 5-degree-of-freedom (DOF) robot arm, a usability study was conducted to observe the impact of the learning system on user performance and satisfaction during a grasping operation. Experimental results show that the system was effective in learning users' grasping intentions, which allowed it to reduce the average time to grasp an object. In addition, participants' feedback from the usability study was generally positive toward having an adaptive robotics system that learns from their commands.

Control barrier functions have been demonstrated to be a useful method of ensuring constraint satisfaction for a wide class of controllers. However, the existing results are mostly restricted to continuous-time systems. Mechanical systems, including robots, are typically second-order systems in which the control occurs at the force/torque level. These systems have actuator, velocity, and position constraints (i.e., relative degree two) that are vital for safety and/or task execution. Additionally, mechanical systems are typically controlled digitally as sampled-data systems. The contribution of this article is twofold. The first contribution is the development of novel, robust control barrier functions that ensure constraint satisfaction for sampled-data systems in the presence of model uncertainty and allows for satisfaction of actuator constraints. The second contribution is the application of the proposed method to the challenging problem of robotic grasping in which a robotic hand must ensure that an object remains inside the grasp while manipulating it to the desired reference trajectory. A grasp constraint satisfying controller is proposed that can admit the existing nominal manipulation controllers from the literature while simultaneously ensuring no slip, no overextension (e.g., singular configurations), and no rolling off of the fingertips. Simulation and experimental results validate the proposed control for the robotic hand application.

In recent times, object detection and pose estimation have gained significant attention in the context of robotic vision applications. Both the identification of objects of interest as well as the estimation of their pose remain important capabilities in order for robots to provide effective assistance for numerous robotic applications ranging from household tasks to industrial manipulation. This problem is particularly challenging because of the heterogeneity of objects having different and potentially complex shapes, and the difficulties arising due to background clutter and partial occlusions between objects. As the main contribution of this work, we propose a system that performs real-time object detection and pose estimation, for the purpose of dynamic robot grasping. The robot has been pre-trained to perform a small set of canonical grasps from a few fixed poses for each object. When presented with an unknown object in an arbitrary pose, the proposed approach allows the robot to detect the object identity and its actual pose, and then adapt a canonical grasp in order to be used with the new pose. For training, the system defines a canonical grasp by capturing the relative pose of an object with respect to the gripper attached to the robot’s wrist. During testing, once a new pose is detected, a canonical grasp for the object is identified and then dynamically adapted by adjusting the robot arm’s joint angles, so that the gripper can grasp the object in its new pose. We conducted experiments using a humanoid PR2 robot and showed that the proposed framework can detect well-textured objects, and provide accurate pose estimation in the presence of tolerable amounts of out-of-plane rotation. The performance is also illustrated by the robot successfully grasping objects from a wide range of arbitrary poses.

To achieve high-accuracy grasping of unknown objects, we present novel multilevel convolutional neural networks (CNNs) for robotic grasping with a parallel gripper or multifingered dexterous hand. The multilevel CNNs include four levels with different structures and functions. The first level is constructed to get the approximate position of the grasped object. The second level aims to obtain the preselected grasping rectangles. The third level is constructed to re-evaluate the preselected grasping rectangles and obtain substantially detailed features with quite a large network, so as to assess each preselected grasping rectangle exactly. By using a selection algorithm, the optimal grasping rectangle can be determined and unknown object grasping can be achieved with a parallel gripper. The purpose of the fourth level is to obtain the finger position distribution to complete the accurate grasping of unknown objects with a multifingered dexterous hand. The test results indicate that, compared to state-of-the-art methods, the proposed multilevel CNNs can greatly increase the precision of the grasping rectangle. Grasping experiments were implemented on a Youbot arm with five degrees of freedom and a Shadow four-fingered dexterous hand. The results show that the multilevel CNNs can determine the optimal grasping rectangle and finger position distribution, thereby achieving high-accuracy grasping of various unknown objects, even under several complex environmental conditions. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners—Robot grasping of objects lags far behind human experiences and poses a significant challenge in the robotics area. To solve it, we present new multilevel convolutional neural networks (CNNs) to process red green blue-depth (RGB-D) images and realize optimal grasping detection of unknown objects. Moreover, we provide details of the network structure, network training, and network testing. The testing results obtained from the open grasping data set show that the multilevel CNNs can significantly increase the accuracy of the grasping rectangle compared to state-of-the-art methods. Experiments were implemented on different robotic platforms, including a five-degrees-of-freedom Youbot arm with a parallel gripper and a UR5 robot arm with a Shadow multifingered dexterous hand. The results validate that the multilevel CNNs offer excellent generalization and robustness for handling different sizes and shapes of unknown objects, as well as background disturbances, which are key problems in robotic manipulation.

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300&#x00D7;300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

In this chapter, we consider problems that arise in designing, building, planning, and controlling operations of robotic hands and end-effectors. The purpose of such devices is often manifold, and it typically includes grasping and fine manipulation of objects in an accurate, delicate yet firm way. We survey the state-of-the-art reached by scientific research and literature about the problems engendered by these often conflicting requirements, and the work that has been done in this area over the last two decades. Because of space limitations, the chapter does not attempt at providing a survey of the technology of robot hands, but rather it is oriented towards covering the theoretical framework, analytical results, and open problems in robotic manipulation.