Free-Viewpoint Images Captured Using Phase-Shifting Synthetic Aperture Digital Holography

Free-viewpoint images obtained from phase-shifting synthetic aperture digital holography are given for scenes that include multiple objects and a concave object. The synthetic aperture technique is used to enlarge the effective sensor size and to make it possible to widen the range of changing perspective in the numerical reconstruction. The lensless Fourier setup and its aliasing-free zone are used to avoid aliasing errors arising at the sensor edge and to overcome a common problem in digital holography, namely, a narrow field of view. A change of viewpoint is realized by a double numerical propagation and by clipping the wave field by a given pupil. The computational complexity for calculating an image in the given perspective from the base complex-valued image is estimated at a double fast Fourier transform. The experimental results illustrate the natural change of appearance in cases of both multiple objects and a concave object

Collective Intelligence for Object Manipulation with Mobile Robots

While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative object manipulation using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning difficulties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a gradient-based soft-body physics simulator into an attention-based neural network, our multi-robot manipulation system can achieve better performance than baselines. In addition, our system also generalizes to unseen configurations during training and is able to adapt toward task completions when external turbulence and environmental changes are applied

GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy

Due to the inherent uncertainty in their deformability during motion, previous methods in rope manipulation often require hundreds of real-world demonstrations to train a manipulation policy for each rope, even for simple tasks such as rope goal reaching, which hinder their applications in our ever-changing world. To address this issue, we introduce GenORM, a framework that allows the manipulation policy to handle different deformable ropes with a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters. At the time of inference, given a new rope, GenORM estimates the deformable rope parameters by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations. With the help of a differentiable physics simulator, we require only a single real-world demonstration. Empirical validations on both simulated and real-world rope manipulation setups clearly show that our method can manipulate different ropes with a single demonstration and significantly outperforms the baseline in both environments (62% improvement in in-domain ropes, and 15% improvement in out-of-distribution ropes in simulation, 26% improvement in real-world), demonstrating the effectiveness of our approach in one-shot rope manipulation

チュウガッコウ ギジュツカ ニオケル ナミ オト オ ダイザイ トシタ ジュギョウ ノ カイハツ

We develop the industrial arts education class about wave and sound, which strength the cooperative study between the subject of science and technology at the junior high school. An outline of the developed class is as follows : In the first lesson, the students observe the amplitude and the frequency with the oscilloscopes and learn the principle of condenser microphone. In the second and third lesson, they learn the soldering method, and manufacture the operation circuit of condenser microphone. In the fourth lesson, the wave form from the operation circuit of condenser microphone manufactured by each student is verified with the oscilloscopes. Moreover, by connecting the operation circuit of condenser microphone with the mini−amplifier whose gain is about 10, the sound as an output is also verified. Finally, in the fifth lesson, the technology and device which control the sound such as an effect unit is explained. The class was successfully carried out among 18 junior high school students, and received generally favorable feedbacks from the school students. From the assessment with the questionnaire investigation, it is indicated that the developed class can be utilized for cooperative study between the subject of science and technology at the junior high school

World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator

Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments

TRAIL Team Description Paper for RoboCup@Home 2023

Our team, TRAIL, consists of AI/ML laboratory members from The University of Tokyo. We leverage our extensive research experience in state-of-the-art machine learning to build general-purpose in-home service robots. We previously participated in two competitions using Human Support Robot (HSR): RoboCup@Home Japan Open 2020 (DSPL) and World Robot Summit 2020, equivalent to RoboCup World Tournament. Throughout the competitions, we showed that a data-driven approach is effective for performing in-home tasks. Aiming for further development of building a versatile and fast-adaptable system, in RoboCup @Home 2023, we unify three technologies that have recently been evaluated as components in the fields of deep learning and robot learning into a real household robot system. In addition, to stimulate research all over the RoboCup@Home community, we build a platform that manages data collected from each site belonging to the community around the world, taking advantage of the characteristics of the community