Experiments with hierarchical reinforcement learning of multiple grasping policies

Robotic grasping has attracted considerable interest, but it still remains a challenging task. The data-driven approach is a promising solution to the robotic grasping problem; this approach leverages a grasp dataset and generalizes grasps for various objects. However, these methods often depend on the quality of the given datasets, which are not trivial to obtain with sufficient quality. Although reinforcement learning approaches have been recently used to achieve autonomous collection of grasp datasets, the existing algorithms are often limited to specific grasp types. In this paper, we present a framework for hierarchical reinforcement learning of grasping policies. In our framework, the lowerlevel hierarchy learns multiple grasp types, and the upper-level hierarchy learns a policy to select from the learned grasp types according to a point cloud of a new object. Through experiments, we validate that our approach learns grasping by constructing the grasp dataset autonomously. The experimental results show that our approach learns multiple grasping policies and generalizes the learned grasps by using local point cloud information

A learning-based shared control architecture for interactive task execution

Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context to simplify the control task for the human operator. To do this prediction, we use Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller’s autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control

Pengalaman Akomodasi Komunikasi (Kasus: Interaksi Etnis Jawa dengan Etnis Batak)

Geografis Indonesia yang terdiri dari ribuan pulau, berada diantara dua benua dan dua samudra, dan pernah menjadi jalur utama perdagangan kuno menjadikan kultur yang dimiliki oleh masyarakat Indonesia menjadi beragam. Keberagaman budaya, selain menjadi anugerah negeri juga menjadi potensi masalah. Potensi masalah yang bisa muncul yaitu kesalahpahaman ketika proses komunikasi antarbudaya, bahkan dalam taraf yang drastis dapat memicu konflik. Kasus yang diangkat merupakan interaksi antara etnis Jawa dengan Batak. Nilai dan norma yang dipegang oleh anggota dari etnis ini dinilai saling bertolak belakang.Penelitian ini bertujuan untuk memahami bentuk akomodasi komunikasi serta kendala yang muncul ketika individu dari etnis Jawa dengan Batak berinteraksi pada tahap perkenalan. Penelitian ini menggunakan paradigma Interpretif dan pendekatan fenomenologi yang digunakan untuk memahami suatu fenomena menurut perspektif informan, dalam hal ini yaitu individu dari etnis Jawa dengan Batak ketika melakukan proses akomodasi komunikasi pada tahap perkenalan. Teori Akomodasi Komunikasi digunakan sebagai alat untuk membaca bentuk akomodasi yang digunakan oleh masing-masing informan. Peneliti menggunakan teknik wawancara mendalam kepada empat informan yang masing-masing berasal dari etnis Jawa dan Batak.Hasil dari penelitian ini menunjukkan bahwa bentuk akomodasi komunikasi yang digunakan oleh individu dari etnis Jawa dan Batak adalah Konvergensi, dimana individu berusaha untuk menyamakan perilaku komunikasi dengan lawan bicaranya. Selama proses komunikasi mereka mengesampingkan atribut-atribut kultural yang mereka miliki dengan tujuan mengakomodasi, hal ini menunjukkan adanya kesadaran untuk melakukan akomodasi pada komunikasi antarbudaya. Kedua etnis ini memiliki perbedaan faktor yang mendorong mereka untuk melakukan akomodasi, individu dari etnis Jawa mengakomodasi karena dorongan kultural, sedangkan individu dari etnis Batak mengakomodasi agar diterima kedalam kelompok. Kendala yang muncul selama proses komunikasi adalah stereotip, penggunaan bahasa, dan kurangnya informasi kultural

Sample and feedback efficient hierarchical reinforcement learning from human preferences

While reinforcement learning has led to promising results in robotics, defining an informative reward function can sometimes prove to be challenging. Prior work considered including the human in the loop to jointly learn the reward function and the optimal policy. Generating samples from a physical robot and requesting human feedback are both taxing efforts for which efficiency is critical. In contrast to prior work, in this paper we propose to learn reward functions from both the robot and the human perspectives in order to improve on both efficiency metrics. On one side, learning a reward function from the human perspective increases feedback efficiency by assuming that humans rank trajectories according to an outcome space of reduced dimensionaltiy. On the other side, learning a reward function from the robot perspective circumvents the need for learning a dynamics model while retaining the sample efficiency of model-based approaches. We provide an algorithm that incorporates bi-perspective reward learning into a general hierarchical reinforcement learning framework and demonstrate the merits of our approach on a toy task and a simulated robot grasping task

Mode-matched ion-exchanged glass-waveguide bridge for high-performance dense wavelength division multiplexer

Abstract-Data bit rate, 1-dB passband, and device dimensions are the key properties of dense wavelength division multiplexing (WDM) devices. For blazed-grating-based dense WDM devices, analysis shows that all these three properties can be enhanced by reducing the output fiber-array channel spacing. In this paper, we propose an ion-exchanged glass waveguide to reduce the output channel spacing. To fabricate the low-loss fiber-compatible waveguide, a field-assisted ion-exchange process is developed Index Terms-Glass waveguide, ion exchange, pulse broadening, wavelength division multiplexing (WDM)

Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Grasping is an essential component for robotic manipulation and has been investigated for decades. Prior work on grasping often assumes that a sufficient amount of training data is available for learning and planning robotic grasps. However, since constructing such an exhaustive training dataset is very challenging in practice, it is desirable that a robotic system can autonomously learn and improves its grasping strategy. In this paper, we address this problem using reinforcement learning. Although recent work has presented autonomous data collection through trial and error, such methods are often limited to a single grasp type, e.g., vertical pinch grasp. We present a hierarchical policy search approach for learning multiple grasping strategies. Our framework autonomously constructs a database of grasping motions and point clouds of objects to learn multiple grasping types autonomously. We formulate the problem of selecting the grasp location and grasp policy as a bandit problem, which can be interpreted as a variant of active learning. We applied our reinforcement learning to grasping both rigid and deformable objects. The experimental results show that our framework autonomously learns and improves its performance through trial and error and can grasp previously unseen objects with a high accuracy