Naohiko Sugita

Bio: Naohiko Sugita is an academic researcher from University of Tokyo. The author has contributed to research in topics: Interferometry & Fizeau interferometer. The author has an hindex of 25, co-authored 228 publications receiving 2347 citations. Previous affiliations of Naohiko Sugita include Nagoya Institute of Technology.

Correction to: Abrupt initiation of material removal by focusing continuous-wave fiber laser on glass

Abstract: In the original publication of the article, Fig. 5 was incorrect. The correct Fig. 5 appears as below.

A parallel robot to assist vitreoretinal surgery.

Abstract: This paper describes the development and evaluation of a parallel prototype robot for vitreoretinal surgery where physiological hand tremor limits performance. The manipulator was specifically designed to meet requirements such as size, precision, and sterilization; this has six-degree-of-freedom parallel architecture and provides positioning accuracy with micrometer resolution within the eye. The manipulator is controlled by an operator with a “master manipulator” consisting of multiple joints. Results of the in vitro experiments revealed that when compared to the manual procedure, a higher stability and accuracy of tool positioning could be achieved using the prototype robot. This microsurgical system that we have developed has superior operability as compared to traditional manual procedure and has sufficient potential to be used clinically for vitreoretinal surgery.

Master-slave robotic platform and its feasibility study for micro-neurosurgery.

Abstract: Background Microsurgery is a widely performed process in neurosurgery. However, it is difficult for surgeons because manipulating small and long instruments under a microscope often restricts dexterity. Hand tremors are also an issue, as the accuracy required for microsurgery is very high. Method A master–slave robotic platform has been developed for neurosurgery. A position–orientation decoupled design was employed to enhance positioning accuracy, and a smooth trajectory generation method was developed. Result The robotic tasks exhibited improved positioning accuracy compared to manual tasks. Anastomoses of 0.3 and 0.5 mm artificial vessels were successfully performed in end-to-end and end-to-side fashion. Conclusion With the robotic platform, the surgeon was able to perform a fine and complex task, which is very difficult with manual operation. The robotic system showed sufficient accuracy and dexterity, but with a longer task completion time. Further improvement of the dexterity and user interface is expected to realize better performance. Copyright © 2012 John Wiley & Sons, Ltd.

Online Trajectory Planning and Force Control for Automation of Surgical Tasks

Abstract: Automation of surgical tasks is expected to improve the quality of surgery. In this paper, we address two issues that must be resolved for automation of robotic surgery: online trajectory planning and force control under dynamic conditions. By leveraging demonstrations under various conditions, we model the conditional distribution of the trajectories given the task condition. This scheme enables generalization of the trajectories of spatial motion and contact force to new conditions in real time. In addition, we propose a force tracking controller that robustly and stably tracks the planned profile of the contact force by learning the spatial motion and contact force simultaneously. The proposed scheme was tested with bimanual tasks emulating surgical tasks that require online trajectory planning and force tracking control, such as tying knots and cutting soft tissues. Experimental results show that the proposed scheme enables planning of the task trajectory under dynamic conditions in real time. In addition, the performance of the force control schemes was verified in the experiments. Note to Practitioners —This paper addresses the problem of motion planning and control for automation of surgical tasks. In surgical tasks, it is necessary to manipulate objects under conditions where positions or shapes of objects often change during the task. Thus, trajectories for surgical tasks need to be planned and updated according to the change in the conditions in real time. In this paper, we propose a framework for learning both spatial motion and force profile from human experts. The proposed system can plan and update task trajectories in real time and robustly control the contact force under dynamic conditions. On the other hand, generalization of trajectories is limited to the conditions, which are close to the conditions where the demonstrations were performed. In the future work, we will investigate reinforcement learning approaches in order to enable autonomous improvement of the performance.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Digital wavefront measuring interferometer for testing optical surfaces and lenses

Abstract: A self-scanned 1024 element photodiode array and minicomputer are used to measure the phase (wavefront) in the interference pattern of an interferometer to lambda/100. The photodiode array samples intensities over a 32 x 32 matrix in the interference pattern as the length of the reference arm is varied piezoelectrically. Using these data the minicomputer synchronously detects the phase at each of the 1024 points by a Fourier series method and displays the wavefront in contour and perspective plot on a storage oscilloscope in less than 1 min (Bruning et al. Paper WE16, OSA Annual Meeting, Oct. 1972). The array of intensities is sampled and averaged many times in a random fashion so that the effects of air turbulence, vibrations, and thermal drifts are minimized. Very significant is the fact that wavefront errors in the interferometer are easily determined and may be automatically subtracted from current or subsequent wavefrots. Various programs supporting the measurement system include software for determining the aperture boundary, sum and difference of wavefronts, removal or insertion of tilt and focus errors, and routines for spatial manipulation of wavefronts. FFT programs transform wavefront data into point spread function and modulus and phase of the optical transfer function of lenses. Display programs plot these functions in contour and perspective. The system has been designed to optimize the collection of data to give higher than usual accuracy in measuring the individual elements and final performance of assembled diffraction limited optical systems, and furthermore, the short loop time of a few minutes makes the system an attractive alternative to constraints imposed by test glasses in the optical shop.

Biomedical applications of soft robotics

Abstract: Soft robotics enables the design of soft machines and devices at different scales. The compliance and mechanical properties of soft robots make them especially interesting for medical applications. Depending on the level of interaction with humans, different levels of biocompatibility and biomimicry are required for soft materials used in robots. In this Review, we investigate soft robots for biomedical applications, including soft tools for surgery, diagnosis and drug delivery, wearable and assistive devices, prostheses, artificial organs and tissue-mimicking active simulators for training and biomechanical studies. We highlight challenges regarding durability and reliability, and examine traditional and novel soft and active materials as well as different actuation strategies. Finally, we discuss future approaches and applications in the field. Soft robots have broad applications in medicine. In this Review, biomedical applications, including surgery, drug delivery, prostheses, wearable devices and artificial organs, are discussed in the context of materials, actuation strategies and challenges.

An Algorithmic Perspective on Imitation Learning

Abstract: As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning. We pay particular attention to the intimate connection between imitation learning approaches and those of structured prediction Daume III et al. [2009]. To structure this discussion, we categorize imitation learning techniques based on the following key criteria which drive algorithmic decisions: 1) The structure of the policy space. Is the learned policy a time-index trajectory (trajectory learning), a mapping from observations to actions (so called behavioral cloning [Bain and Sammut, 1996]), or the result of a complex optimization or planning problem at each execution as is common in inverse optimal control methods [Kalman, 1964, Moylan and Anderson, 1973]. 2) The information available during training and testing. In particular, is the learning algorithm privy to the full state that the teacher possess? Is the learner able to interact with the teacher and gather corrections or more data? Does the learner have a (typically a priori) model of the system with which it interacts? Does the learner have access to the reward (cost) function that the teacher is attempting to optimize? 3) The notion of success. Different algorithmic approaches provide varying guarantees on the resulting learned behavior. These guarantees range from weaker (e.g., measuring disagreement with the agent’s decision) to stronger (e.g., providing guarantees on the performance of the learner with respect to a true cost function, either known or unknown). We organize our work by paying particular attention to distinction (1): dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control or inverse reinforcement learning [Russell, 1998]). In the latter case, behavior arises as the result of an optimization problem solved for each new instance that the learner faces. In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples—such as robots that play table tennis [Kober and Peters, 2009], programs that play the game of Go [Silver et al., 2016], and systems that understand natural language [Wen et al., 2015]— illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions for machine learning.