Maeda Lab: 2022

MAEDA Lab

INTELLIGENT & INDUSTRIAL ROBOTICS

Maeda Lab: 2022–2023

Div. of Systems Research, Faculty of Engineering / Specialization in Mechanical Engineering, Dept. of

Mechanical Engineering, Materials Science, and Ocean Engineering, Graduate School of Engineering Science /

Interfaculty Graduate School of Innovative and Practical Studies /

Dept. of Mechanical Engineering, Materials Science, and Ocean Engineering, College of Engineering Science,

Yokohama National University

Mechanical Engineering and Materials Science Bldg. (N6-5),

79-5 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501 JAPAN

Tel/Fax +81-45-339-3918 (Prof. Maeda)/+81-45-339-3894 (Lab)

E-mail maeda[at]ynu.ac.jp

https://iir.ynu.ac.jp/

People (2022–2023 Academic Year)

• Dr. Yusuke MAEDA (Professor, Div. of Systems Research, Fac. of Engineering)

• Doctoral Students (Specialization in Mechanical Engineering, Dept. of Mechanical

Engineering, Materials Science, and Ocean Engineering, Graduate School of Engineering

Science)

– Reiko TAKAHASHI (JSPS Research Fellow)

• Master’s Students (Specialization in Mechanical Engineering, Dept. of Mechanical

Engineering, Materials Science, and Ocean Engineering, Graduate School of Engineering

Science/Interfaculty Graduate School of Innovative and Practical Studies)

– Akitoshi SAKATA

– Akihide SUGA

– Naruya SUZUKI

– Kenta TAKAHASHI

– Yuta NAKANISHI

– Yoshiki TAHARA

– Haruki KAMIKUKITA

– Kenta SAKAKI

– Mizuki SHONO

– Itsuma MATSUI

– Pedro SAMAN

• Undergraduate Students (Mechanical Engineering Program, Dept. of Mechanical

Engineering, Materials Science, and Ocean Engineering, College of Engineering Science)

– Fumiya ENDO

– Rion SATO

– Naoya TAKAHASHI

– Shogo MIKAMI

2022–2023 Maeda Lab, Yokohama National University

SLAM-Integrated Kinematic Calibration (SKCLAM)

SLAM (Simultaneous Localization and Mapping) techniques can be applied to industrial manip-

ulators for 3D mapping around them and calibration of their kinematic parameters. We call this

“SKCLAM” (Simultaneous Kinematic Calibration, Localization and Mapping). Using an RGB-

D camera attached to the end-effector of a manipulator (Fig. 1), we demonstrated successful

SKCLAM in a virtual environment (Fig. 2) and a real environment (Fig. 3) [1][2]. We are also

studying SKCLAM with spherical cameras [3] and stereo cameras [4].

References

[1] J. Li, A. Ito, H. Yaguchi and Y. Maeda: Simultaneous kinematic calibration, localization, and mapping

(SKCLAM) for industrial robot manipulators, Advanced Robotics, Vol. 33, No. 23, pp. 1225–1234, 2019.

[2] A. Ito, J. Li and Y. Maeda: SLAM-Integrated Kinematic Calibration Using Checkerboard Patterns, Proc.

of 2020 IEEE/SICE Int. Symp. on System Integration (SII 2020), pp. 551–556, 2020.

[3] Y. Tanaka, J. Li, A. Ito and Y. Maeda: SLAM-Integrated Kinematic Calibration with Spherical Cameras

for Industrial Manipulators, Proc. of JSME Conf. on Robotics and Mechatronics 2020 (ROBOMECH

2020), 2P2-B05, 2020 (in Japanese).

[4] Y. Nagatomo, J. Li, Y. Tanaka and Y. Maeda: SLAM-integrated Kinematic Calibration with a Stereo

Camera for Industrial Robots, Proc. of JSME Conf. of Manufacturing Systems Division 2021, pp. 77–

78, 2021 (in Japanese).

Fig. 1 Manipulator Equipped

with an RGB-D Camera

Fig. 2 SKCLAM in Virtual Environment

Fig. 3 Example of an Obtained 3D Map

2022–2023 Maeda Lab, Yokohama National University

Robot Teaching

Teaching is indispensable for current industrial robots to execute tasks. Human operators have to

teach motions in detail to robots by, for example, conventional teaching/playback. However, robot

teaching is complicated and time-consuming for novice operators and the cost for training them

is often unaffordable in small-sized companies. Thus we are studying easy robot programming

methods toward the dissemination of robot utilization.

Robot programming with manual volume sweeping We developed a robot programming

method for part handling [1][2]. In this method, a human operator makes a robot manipulator

sweep a volume by its bodies. The swept volume stands for (a part of) the manipulator’s free

space, because the manipulator has passed through the volume without collisions. Next, the

obtained swept volume is used by a motion planner to generate a well-optimized path of the

manipulator automatically. The swept volume can be displayed with Augmented Reality (AR)

so that human operators can easily understand it, which leads to efﬁcient robot programming

[3] (Fig. 4).

Assisting Online Robot Programming We are developing a support system for online robot

programming using an optical see-through AR device that can overlay useful information on

a real robot such as its movable area [4] (Fig. 5). Another support system for online robot

programming is also developed. In this system, it is possible to group and move existing teach-

ing points, and generate robot motions that connect the points. This is useful for adaptation to

product speciﬁcation changes in robotic assembly [5].

References

[1] Y. Maeda, T. Ushioda and S. Makita: Easy Robot Programming for Industrial Manipulators by Manual

Volume Sweeping, Proc. of 2008 IEEE Int. Conf. on Robotics and Automation (ICRA 2008), pp. 2234–

2239, 2008.

[2] S. Ishii and Y. Maeda: Programming of Robots Based on Online Computation of Their Swept Vol-

umes, Proc. of 23rd IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN 2014),

pp. 385–390, 2014.

[3] Y. Sarai and Y. Maeda: Robot Programming for Manipulators through Volume Sweeping and Augmented

Reality, Proc. of 13th IEEE Conf. on Automation Science and Engineering (CASE 2017), pp. 302–307,

2017.

[4] K. Takahashi and Y. Maeda: Implementation and Evaluation of a System for Robot Teaching Support

Using an Optical See-Through AR Device, Proc. of 2021 JSME Conf. on Robotics and Mechatronics

(ROBOMECH 2021), 2P3-A02, 2021 (in Japanese).

[5] H. Ihara and Y. Maeda: A Robot Programming System with Teach Point Manipulation and Motion Plan-

ning to Adapt Product Speciﬁcation Change, Proc. of SICE 22nd Conf. on System Integration (SI2021),

pp. 3263–3267, 2021 (in Japanese).

Fig. 4 AR Display of Swept Volume and Planned Path

Fig. 5 AR Display of Movable

Area with Fixed Gripper Pose

2022–2023 Maeda Lab, Yokohama National University

View-Based Teaching/Playback

We developed a teaching/playback method based on camera images for industrial manipulators

[1][2]. In this method, robot motions and scene images in human demonstrations are recorded to

obtain an image-to-motion mapping, and the mapping is used for playback (Fig. 6). It can achieve

more robustness against changes of task conditions than conventional joint-variable-based teach-

ing/playback. Our method adopts end-to-end learning through view-based image processing and

therefore neither object models nor camera calibration are necessary. We are improving our view-

based teaching/playback by using range images (Fig. 7) and occlusion-aware techniques for more

robustness [3]. For application to force-control tasks, visualization of force information based on

photoelasticity (Fig. 8) is under investigation [4]. We are also trying to integrate reinforcement

learning with the view-based teaching/playback for reduction of human operations for teaching

[5].

References

[1] Y. Maeda and T. Nakamura: View-based teaching/playback for robotic manipulation, ROBOMECH J.,

Vol. 2, 2, 2015.

[2] Y. Maeda and Y. Moriyama: View-Based Teaching/Playback for Industrial Manipulators, Proc. of 2011

IEEE Int. Conf. on Robotics and Automation (ICRA 2011), pp. 4306–4311, 2011.

[3] Y. Maeda and Y. Saito: Lighting- and Occlusion-robust View-based Teaching/Playback for Model-free

Robot Programming, W. Chen et al. eds., Intelligent Autonomous Systems 14, pp. 939–952, Springer,

2017.

[4] Y. Nakagawa, Y. Maeda and S. Ishii: View-Based Teaching/Playback with Photoelasticity for Force-

Control Tasks, W. Chen et al. eds., Intelligent Autonomous Systems 14, pp. 825–837, Springer, 2017.

[5] Y. Maeda and R. Aburata: Teaching and Reinforcement Learning of Robotic View-Based Manipulation,

Proc. of 22nd IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN 2013), pp.

87–92, 2013.

camera

robot

object

human instruction

(a) human teaching

mapping

image

robot

motion

(b) image-to-motion mapping

camera

robot

object

mapping

Fig. 6 Outline of View-Based Teaching/Playback

0 100 200 300 400 500

frame number

Grayscale

Images

Light

OFF

Range

Images

Fig. 7 Switching between Grayscale and Range

Images for View-Based Teaching/Playback

Fig. 8 View-based Teaching/

Playback with Photoelasticity

2022–2023 Maeda Lab, Yokohama National University

Caging and Caging-based Grasping

Caging is a method to constrain objects geometrically so that they cannot escape from a “cage”

constituted of robot bodies.

3D multiﬁngered caging While most of related studies deal with planar caging, we study three-

dimensional caging by multiﬁngered robot hands (Fig. 9). Caging does not require force con-

trol, and therefore it is well-suited to current robotic devices and contributes to provide a variety

of options of robotic manipulation. We are investigating sufﬁcient conditions for 3D multi-

ﬁngered caging and developing an algorithm to plan hand motions for caging based on the

conditions [1]. Robot motions generated by the developed planning algorithm were validated

on an arm-hand system [2] (Fig. 10).

Caging-based Grasping Position-controlled robot hands can capture an object and manipulate

it via caging without force sensing or force control. However, the object in caging is movable in

the closed region, which is not allowed in some applications. In such cases, grasping is required.

We proposed a new simple approach to grasping by position-controlled robot hands: caging-

based grasping by robot ﬁngers with rigid parts and outer soft parts. In caging-based grasping,

we cage an object with the rigid parts of a robot hand, and construct a complete grasp with

the soft parts of the hand. We are studying the formal deﬁnition of the caging-based grasping

and concrete conditions for caging-based grasping in planar and spatial cases. Based on the

derived conditions, we demonstrated planar caging-based grasping by mobile robots and spatial

caging-based grasping by a multiﬁngered hand (Fig. 11) [3][4]. We also extend the theory of

caging-based grasping so that it can deal with deformable objects (Fig. 12) ([5]).

References

[1] S. Makita and Y. Maeda: 3D Multiﬁngered Caging: Basic Formulation and Planning, Proc. of 2008

IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2008), pp. 2697–2702, 2008.

[2] S. Makita, K. Okita and Y. Maeda: 3D Two-Fingered Caging for Two Types of Objects: Sufﬁcient

Conditions and Planning, Int. J. of Mechatronics and Automation, Vol. 3, No. 4, pp. 263–277, 2013.

[3] Y. Maeda, N. Kodera and T. Egawa: Caging-Based Grasping by a Robot Hand with Rigid and Soft Parts,

Proc. of 2012 IEEE Int. Conf. on Robotics and Automation (ICRA 2012), pp. 5150–5155, 2012.

[4] T. Egawa, Y. Maeda and H. Tsuruga: Two- and Three-dimensional Caging-Based Grasping of Objects

of Various Shapes with Circular Robots and Multi-Fingered Hands, Proc. of 41st Ann. Conf. of IEEE

Industrial Electronics Soc. (IECON 2015), pp. 643–648, 2015.

[5] D. Kim, Y. Maeda and S. Komiyama: Caging-based Grasping of Deformable Objects for Geometry-

based Robotic Manipulation

, ROBOMECH J., Vol. 6, 3, 2019.

Fig. 9 3D Multiﬁn-

gered Caging

Fig. 10 Caging of a

Sphere

Fig. 11 Caging-

based Grasping by a

Multiﬁngered Hand

Fig. 12 Caging-

based Grasping of a

Deformable Object

2022–2023 Maeda Lab, Yokohama National University

Caging Manipulation

Caging is a method to make an object inescapable from a closed region geometrically. We study

robotic manipulation with caging, or “caging manipulation.”

In-Hand Caging Manipulation Pose of objects caged in robot hands can be controlled to some

extent by changing hand conﬁgurations. We call it “in-hand caging manipulation.” It enables

position-controlled robot hands to perform robust in-hand manipulation. A planning algorithm

for in-hand caging manipulation was developed [1][2]. We are also studying various forms of

in-hand caging manipulation [3] including versatile part feeders [4] (Fig. 13).

Cooperative Caging Manipulation The object is not fully constrained in caging. This nature

enables cooperative manipulation based on position control without excessive internal forces.

We study dual-arm cooperative manipulation of long objects with caging or caging-based grasp-

ing (Fig. 14) [5]. It does not require force control, and can deal with a variety of objects by

using appropriate end-effectors.

References

[1] Y. Maeda and T. Asamura: Sensorless In-hand Caging Manipulation, W. Chen et al. eds., Intelligent

Autonomous Systems 14, pp. 255–267, Springer, 2017.

[2] S. Komiyama and Y. Maeda: Position and Orientation Control of Polygonal Objects by Sensorless

In-hand Caging Manipulation, Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA 2021),

pp. 6244–6249, 2021.

[3] Y. Maeda, T. Asamura, T. Egawa and Y. Kurata: Geometry-Based Manipulation through Robotic Caging,

IEEE/RSJ IROS 2014 Workshop on Robot Manipulation: What has been achieved and what remains to

be done?, 2014.

[4] H. Kamikukita, Y. Nakanishi and Y. Maeda: Realization of a General-purpose Part Feeder with Sensor-

less In-hand Caging Manipulation, Proc. of SICE 22nd Conf. on System Integration (SI2021), pp. 3246–

3248, 2021 (in Japanese).

[5] Y. Hiraki and Y. Maeda: Caging-based Dual-arm Cooperation without Force Control, Proc. of JSME

Conf. on Robotics and Mechatronics 2020 (ROBOMECH 2020), 2A1-M05, 2020 (in Japanese).

Fig. 13 A Versatile Part

Feeder with In-Hand Caging

Manipulation

(a) wire harness

(b) long pipe

Fig. 14 Dual-arm Cooperative Manipulation with Caging

2022–2023 Maeda Lab, Yokohama National University

Handling of Various Objects by Robots

Techniques for robotic manipulation of a variety of objects are under investigation.

Vision-Based Object Picking Robotic bin-picking is more ﬂexible and versatile than the use

of conventional part feeders, and therefore it is effective for low-volume production. Many

bin-picking techniques have been proposed, and some of them are in actual use. However, it

is difﬁcult to apply these existing techniques to coil springs, due to their shape characteristics.

Thus we developed a dedicated method to recognize and localize coil springs in a pile, which

enabled robotic bin-picking of coil springs (Fig. 15) [1]. Additionally we are developing an

impacting-based method to detect unknown objects for picking (Fig. 16) [2].

3D Block Printing We developed a robotic 3D printer: a robot system that can assemble toy

brick sculptures from their 3D CAD models [3][4][5]. In this system, a 3D CAD model is auto-

matically converted to a block model consisting of primitive toy blocks. Then an assembly plan

of the block model is automatically generated, if feasible. According to the plan, an industrial

robot assembles a brick sculpture layer by layer from bottom to top. We demonstrate successful

assembly of several brick sculptures (Fig. 17).

References

[1] K. Ono, T. Ogawa, Y. Maeda, S. Nakatani, G. Nagayasu, R. Shimizu and N. Ouchi: Detection, Lo-

calization and Picking Up of Coil Springs from a Pile, Proc. of 2014 IEEE Int. Conf. on Robotics and

Automation (ICRA 2014), pp. 3477–3482, 2014.

[2] Y. Maeda, H. Tsuruga, H. Honda and S. Hirono: Unknown Object Detection by Punching: An Impacting-

based Approach to Picking Novel Objects, M. Strand et al. eds., Intelligent Autonomous Systems 15,

pp. 668–678, Springer, 2018.

[3] Y. Maeda, O. Nakano, T. Maekawa and S. Maruo: From CAD Models to Toy Brick Sculptures: A

3D Block Printer, Proc. of 2016 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2016),

pp. 2167–2172, 2016.

[4] C. Sugimoto, Y. Maeda, T. Maekawa and S. Maruo: A 3D Block Printer Using Toy Bricks for Various

Models, Proc. of 13th IEEE Conf. on Automation Science and Engineering (CASE 2017), pp. 958–963,

2017.

[5] M. Kohama, C. Sugimoto, O. Nakano and Y. Maeda: Robotic Additive Manufacturing with Toy Blocks,

IISE Trans., Vol. 53, No. 3, pp. 273–284, 2021.

Fig. 15 Bin-Picking of Coil Springs Fig. 16 Impacting-based Picking

Fig. 17 3D Block Printing

2022–2023 Maeda Lab, Yokohama National University

Intelligent Heavy Equipment Systems

Automation and intellitization of heavy machinery is immensely demanded for higher efﬁciency

and safety. We study trafﬁc control of dump truck ﬂeets in mines (Fig. 18) to improve productivity.

A combinatorial optimization method is developed for the order of passing intersections and tested

on a simulator (Fig. 19) [1][2]．

References

[1] Y. Ogawa, Y. Maeda, Y. Matsui, A. Sakai, K. Osagawa and K. Takeda: Trafﬁc Control of Dump Truck

Fleets at Intersections for Mining Productivity Improvement, Trans. of JSME, Vol. 87, No. 894, 20-

00097, 2021 (in Japanese).

[2] Y. Maeda, Y. Ogawa, K. Osagawa, A. Sakai and Y. Matsui: Worksite Management System And Worksite

Management Method, World patent application WO/2021/145392.

7 6

4321

DUMP

LOAD

STANDBY

Fig. 18 Dump Truck Fleets in a Mine Fig. 19 A Simulator of Dump Truck Fleets

2022–2023 Maeda Lab, Yokohama National University

Modeling and Measurement of Human Hands and Their Dex-

terity

The theory of robotic manipulation can be applied to analysis of human hands and their dexterity.

Understanding of human dexterity is very important to implement high dexterity on robots. We

are conducting some studies on modeling of human hands and skills jointly with Living Activity

Modeling Research Team, AIST.

Digital Hands We are developing a method for generating models of human hands that have

links and skins (Fig. 20) to represent their motion using motion capture [1]. The applications of

the hand models include modeling range of motion with subjective discomfort in human hands

[2].

Grasp Measurement and Synthesis Digital hands can be used to synthesize grasps for sup-

porting ergonomic product design (Fig. 21) [3]. We also study a simple grasp measurement

device (Fig. 22) [4] and grasp synthesis for ROM-limited hands [5].

References

[1] N. Miyata, Y. Shimizu, Y. Motoki, Y. Maeda and M. Mochimaru: Hand MoCAP by Building an Indi-

vidual Hand Model, Int. J. of Human Factors Modelling and Simulation, Vol. 3, No. 2, pp. 147–168,

2012.

[2] N. Miyata, Y. Yoneoka and Y. Maeda: Modeling the Range of Motion and the Degree of Posture Discom-

fort of the Thumb Joints, S. Bagnara et al. eds., Proceedings of the 20th Congress of the International

Ergonomics Association (IEA 2018), Volume V: Human Simulation and Virtual Environments, Work

With Computing Systems (WWCS), Process Control, pp. 324–329, Springer, 2018.

[3] T. Hirono, N. Miyata and Y. Maeda: Grasp Synthesis for Variously-Sized Hands Using a Grasp Database

That Covers Variation of Contact Region, Proc. of 3rd Int. Digital Human Modeling Symp. (DHM 2014),

11, 2014.

[4] N. Miyata, K. Honoki, Y. Maeda, Y. Endo, M. Tada and Y. Sugiura: Wrap & Sense: Grasp Capture by

a Band Sensor, Adjunct Proc. of 29th Annual Symp. on User Interface Software and Technology (UIST

2016), pp. 87–89, 2016.

[5] R. Takahashi, N. Miyata, Y. Maeda and Y. Nakanishi: Grasp Synthesis Considering Graspability for a

Digital Hand with Limited Thumb Range of Motion, Advanced Robotics, Vol. 36, No. 4, pp. 192–204,

2022.

Fig. 20 Model of a

Human Hand

Robust

Gracile

LongShort

Fig. 21 Grasp Synthesis for

Various Hands

Fig. 22 “Wrap & Sense”

2022–2023 Maeda Lab, Yokohama National University

Application of Robot Technology to Human Activity Support

Robot technology should be applied to various ﬁelds to support human activities. For example,

home appliances would be robotized more and more to help our daily life intelligently and ef-

fectively. We have a proposal on smart dishwashers: our proposed system can support users’

dishwasher loading [1][2][3]. This system can recognize dishes from a picture of a dining table

after a meal. Then the system calculates the optimal placement of the recognized dishes in the

dishwasher and presents the result to users as 3D graphics (Fig. 23).

We are also developing a support system for human origami folding [4]. It is composed of an

origami simulator for design and display of origami folding processes (Fig. 24) and a cutting

plotter for adding crease pattern automatically. The system can be used in childhood education

and elderly care.

References

[1] Y. Kurata and Y. Maeda: Toward a Smart Dishwasher: A Support System for Optimizing Dishwasher

Loading, IPSJ SIG Technical Report, Vol. 2016-CDS-16, No. 9, 2016 (in Japanese).

[2] K. Imai and Y. Maeda: A User Support System That Optimizes Dishwasher Loading, Proc. of 2017 IEEE

6th Global Conf. on Consumer Electronics (GCCE 2017), pp. 523–524, 2017.

[3] Y. Ogawa and Y. Maeda: Support of Dishwasher Loading by Counting the Number of Dishes with Image

Processing, Proc. of JSME Conf. on Robotics and Mechatronics 2018 (ROBOMECH 2018), 2A2-J17,

2018 (in Japanese).

[4] N. Suzuki, Y. Nakajima and Y. Maeda: An Origami Assist System by Simulating Origami with Papers

of Nonzero Thickness and Presenting Folding Processes using Augmented Reality, Proc. of JSME Conf.

on Robotics and Mechatronics (ROBOMECH 2021), 2A1-M06, 2021 (in Japanese).

(a) Calculated Result (b) Loaded Dishes

Fig. 23 Optimized Dish Loading Fig. 24 Origami Simulator