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3. MacDonald, J. et al. Alternatives for landmine detection. Report No. RAND/MR-1608-OSTP (RAND Corp.,
Santa Monica, California, 2003).
4. Fischer, R., Burlage, R., DiBenedetto, J. & Maston, M.
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landmines. Technical Report No. ERDC TR-00-5. (USArmy Corps of Engineers, 2000).
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Fundamentals And Applications (Springer, New York;
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Robotic crowd biology with Maholo
LabDroids
To the Editor:
Concerns about the lack of reproducibilityof experimental research1,2, the numerous
labor-intensive tasks required for highthroughput research, and the dangers (andcosts) associated with experiments involving
pathogens and harmful reagents led us to set
up a Robotic Biology Consortium. We believethat the use of humanoid robots (which weterm ‘LabDroids’) to carry out life science
research experiments has the potential tominimize the above problems.
In 2009, the first robot scientist capable ofdevising hypotheses and then testing them
was reported. By integrating automation
technologies and data analysis pipelines, therobot, named Adam, was able to find new(and subsequently validated) information inyeast functional genomics experiments3. Morerecently, a robotic system was developed toenable precise and dexterous experiments withthe model insect Drosophila4. Today, several
startups, including Transcriptic (Menlo Park,
CA, USA) and Emerald Cloud Lab (S. San
Francisco, CA, USA), offer researchers remote
access to laboratory automation systems tocarry out experiments5. However, current
laboratory automation systems are fixed
assemblies of job-specific modules and canenable only a limited suite of experiments.
To establish a versatile laboratory
automation system, we developed a highperformance LabDroid system named‘Maholo’ (Fig. 1a and Supplementary Video;
technical specifications to be publishedelsewhere). Maholo has one torso pivot andtwo arms, each of which has seven rotationalaxes and can manipulate laboratory tools
and instruments without robot-specificmodifications. The automation of various
laboratory operations was first simulated using
a computer-aided design system (Fig. 1b)
and calibrated in a real environment. Maholo
can carry out most regular tasks involving
liquid handling in test tubes, Petri dishes,and microplate wells with the same pipettes
310

and aspirators that human researchers andtechnicians use. The automated workflows
for these tasks include a cell-harvestingprocess that requires delicate sample handling(Fig. 1c). In this process Maholo collects acell culture dish from a CO2 incubator andaspirates the culture medium before detaching
the cells by trypsinization, then scrapes them
free using a spatula with the correct pressure
and speed, before collecting the cells in a tube.
Unlike other laboratory automation systems,
Maholo can reproduce human maneuvers
without the assistance of action-specificjigs. Throughout cell culture harvesting, theleft hand of Maholo serves as a jig to holdthe culture dish, whereas the right hand
performs several different tasks including
dispensing, scraping, and transferring the cellsuspension from dish to tube. This jig-freesystem enables greater flexibility in automating
various laboratory protocols with a single
robotic system. Force and vision sensors
allow our LabDroid to manipulate othertools and devices, including a vortex mixer,
sample mixers, tube rotors, incubators andrefrigerators (Fig. 1d). For example, Maholo
can open and shut a centrifuge door, pressac Obtain culture dish Aspirate medium

the front panel buttons and adjust the rotor
angle with the aid of its vision system (Fig. 1e).
Maholo can load tubes in a centrifuge inproperly balanced positions and can salvage
samples from the rotor, stopping at a random
angle (Fig. 1e).
In any experiment that involves manual
handling of reagents, it is inevitable that data
variation will arise owing to human error.
This variability could be minimized by theconsistent movements, operation timings,
and spatial trajectories of a LabDroid. One
could argue that non-humanoid automation
systems, such as liquid handling robots thatcan manipulate microtiter plates, could
outperform LabDroids in precision andscalability for specific tasks. However, it
is more challenging to automate complex
workflows by combining such isolated jobspecific automation systems, each of which
is designed for human manipulation withminimal hardware and software engineering.
Numerous tools in various combinations areused in the laboratory daily. Enormous efforts
have already been devoted to optimizing thesetools for human use. The cost of developing
an integrated laboratory automation systemwith compatible modules to replace all of
the experiments carried out in a typicallaboratory would be prohibitively high.
Instead, we propose that LabDroids, which canmanipulate existing laboratory instruments,
could form the basis of a versatile, scalable, andsustainable laboratory automation system thatmany laboratories might adopt.
The potential of LabDroids underpins our
vision of robotic crowd biology, in which a
crowd of LabDroids and an assortment of
instruments in a large laboratory space areoperated remotely online (Fig. 2a). LabDroids
could be scaled to provide a versatile systemcomprising a team of droids that carry outb

Scrape cellsdHarvest cellseOpen door

Load sample

Push button

Figure 1 LabDroid system. (a) Maholo system. (b) Computer-aided design software system defines
Maholo’s job operations. (c) Demonstration of automated cell-harvesting process. Cell culture dish is
obtained from a CO2 incubator; supernatant is aspirated; and upon trypsinization, cells are collectedusing a spatula and a pipette. (d) Examples of tools Maholo manipulates: (top left) vortex mixer, (topright) sample mixer, (bottom left) rotor, (bottom right) refrigerator. (e) Centrifugation process.

VOLUME 35 NUMBER 4 APRIL 2017 NATURE BIOTECHNOLOGYCORRESPONDENCE
a

b

m

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mit

unit
m
it
e

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xpe
rim
e

bm

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e Protocols in clouds
Dep

osit

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Researcher(replication study)

Do
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Researcher© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.

RCBL

rim
en

roid

er

pap

ts

Sub

LabD

s

ent

erim

xp
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CentralcomputingsystemRobotic Crowd Biology Laboratory (RCBL)

Researcher(new experiments)

Protocolsfor publication

Figure 2 Robotic crowd biology concept. (a) RCBL, a large laboratory automation field with a numberof LabDroids manipulating a wide range of laboratory tools, instruments, and job-specific automation
systems. Each LabDroid is allocated a different set of instruments and a central computing system
dynamically schedules the cooperation of multiple LabDroids. (b) Researcher designs and submits
protocols online to an RCBL. The same protocols can be provided to the public in paper publications.
(c) Published experiments can be replicated by downloading the protocols and resubmitting them to thesame or another RCBL. (d) New experiments can be efficiently constructed by rewiring existing protocol
resources. (e) Sustainable model for reusing and expanding protocols published by journals.

job-specific laboratory automation systems
combined with human-usable tools. In
such a robotic crowd (or cloud) biology
laboratory (RCBL), experimental protocols
are sent online and samples are shippedfrom all over the world (Fig. 2b). Reagentsand samples are barcoded and linked tothe corresponding process in the protocols.
Barcode scanning and automated internaldelivery systems allocate reagents and samples
around LabDroids that operate experiments.
A timeline of each experimental step is
extracted automatically from the submitted
protocol, with information for materials andinstruments. A single LabDroid does not
perform a whole experiment by itself, but
a team of LabDroids can perform multiple
experiments simultaneously based on an‘agent crowd operation’. For each sub-process
in the different experiments in the queue, thecentral computing system dynamically assigns
available LabDroids that are close to optimal
instruments and thereby maximizes the
production of the whole RCBL.
The development of an RCBL could
provide a new way of doing life science
research. All materials and methods
implemented in an RCBL would be complete
and robust. The availability of crowdsourced
protocols that are continually recorded,
tested, and validated by robotic systems that
automatically report each experiment might
alleviate the reproducibility crisis by having
protocols described in a standard notationthat could be attached to manuscripts
submitted to journal publishers, and in turnserve as primary resources for replicationand modification studies (Fig. 2b,c). Using
protocol editing software, new protocols
could be efficiently developed by modifying
different layers of existing job and workflow
modules stored in online protocol archives,in which the number of reutilizations foreach module can be traced (Fig. 2d,e),
as an alternative to evaluating researcher
contributions. Furthermore, researchers
could outsource large-scale experiments
to an RCBL by applying a range ofmodifications to a pilot experiment carriedout in their own laboratory. Similarly, highbiosafety-level studies could be readily
executed by LabDroids.
Using an RCBL, instruments could
be shared, and waiting time and space
surplus could be minimized by the centralcomputing system’s dynamic optimizationof resource assignments. Plans for installing,
replacing, and leasing equipment could becomputed from the experiment history andtrend information. The RCBL actualizes‘lab-less’ research; once process automations
accumulate with their reutilization andreliability information to a certain threshold,precision experiments can be designed andexecuted without testing the performanceof each robotic operation. This might
conceivably provide access to life science
research by a broad community of people,
for example, HIV research by high-school
scientists.
To scale the current single LabDroid-based
system up to an RCBL, a standard framework

NATURE BIOTECHNOLOGY VOLUME 35 NUMBER 4 APRIL 2017

needs to be developed for describing
experimental protocols, operating systemassemblies, and recording results. Currently,
Maholo’s proprietary software system andapplication program interface enables only
Maholo to perform experimental tasks.
Although robotic operation frameworks
are also provided by several services,
including Transcriptic, Emerald Cloud
Lab, Aquarium (a product of Eric Klaven’s
group at the University of Washington,
Seattle) and Synthace’s (London) Antha, nopractical framework has been proposed totransfer protocols among different laboratory
automation systems or to freely integratedifferent tools and automation systems toautomate complex experimental operations.
We propose that a scalable laboratory
automation should be split into two layers:
first, a standard semantic layer, or processontology layer, in which experimental
workflows are described by connecting various
job processes (‘process description’); andsecond, a translation layer, in which each job
process described in the standard semantics
is compiled into robotic operations for agiven operation environment (‘process-tooperation mapping’). The first layer could be
actualized from an open science community
by harnessing previous efforts for processontology for laboratory experiments6–9. The
second layer would require the manufacturingside to prepare its process-to-operation
mapping for different job processes definedusing the standard syntax.
At present, laboratory automation processes
generally suffer from the ‘hard coding’ ofsystems and manual adjustments to sensitiveenvironmental differences, which are usually
not well documented. However, for LabDroids
that have sensing systems and form an RCBL,
robotic behaviors in a specified environment
could be fed back to the central computing
system and automatically optimized withthe support of artificial intelligence. This
would allow the ideal automated processto-operation mapping or ‘real world
programming of experiments’ (much likecompiling a program script in a particular
computational hardware environment).
Phase I of the Robotic Biology Consortium
is to build a few small-scale RCBLs by early2020. Each RCBL will be composed ofmultiple LabDroids, laboratory automation
systems, and human-usable experimental tools
and equipment. We plan to demonstrate fully
remote operation of complex experiments ingenomics, proteomics, and high-content cell
screening, and to showcase the reproducibilityof the experiments exchanged between
different RCBLs.
311

CORRESPONDENCE
Although substantial challenges remain,such as the establishment of widely agreed
upon standard semantics, massively parallel
operation of robotic crowds and instruments,
and the implementation of artificial
intelligence, robotic crowd biology using the
LabDroid-centered system has the potential
to scale current life science and laboratory
automation in a robust and reproducible way.

© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.

Editor’s note: This article has been peer-reviewed.
Note: Any Supplementary Information and Source Data
files are available in the online version of the paper.
ACKNOWLEDGMENTS
We greatly thank H. Maruyama for early discussion
on the concept of robotic crowd biology. N.Y. was
supported by PRESTO, Japan Science and Technology
Agency.
AUTHOR CONTRIBUTIONS
N.Y. formulated the grand design of robotic
crowd biology, prepared the figures, and wrote themanuscript. T.N. led the development of Maholowith the Robotic Biology Consortium and wrote themanuscript.
COMPETING FINANCIAL INTERESTS
The authors declare competing financial interests:
details are available in the online version of the paper.

Nozomu Yachie1–5, Robotic Biology
Consortium6 & Tohru Natsume7,8
1Research Center for Advanced Science andTechnology, the University of Tokyo, Tokyo,
Japan. 2Department of Biological Sciences,
Graduate School of Science, the University ofTokyo, Tokyo, Japan. 3Institute for Advanced
Biosciences, Keio University, Tsuruoka, Japan.
4PRESTO, Japan Science and Technology Agency,
Tokyo, Japan. 5Spiber, Inc., Tsuruoka, Japan.
6A full list of members and affiliations follows.
7Molecular Profiling Research Center for Drug
Discovery, National Institute of Advanced
Industrial Science and Technology, Tokyo, Japan.
8Robotic Biology Institute, Inc., Tokyo, Japan.
9RIKEN Quantitative Biology Center,
Osaka, Japan. 10Database Center for Life
Science (DBCLS), Joint Support-Center for
Data Science Research, Research Organizationof Information and Systems (ROIS), Kashiwa,
Japan. 11Japan Research Industries andIndustrial Technology Association, Tsukuba,
Japan. 12The Systems Biology Institute, Tokyo,
Japan. 13Sony Computer Science Laboratories,
Inc., Tokyo, Japan. 14RIKEN Center for
Integrative Medical Sciences, Kanagawa,Japan. 15Okinawa Institute of Science andTechnology, Okinawa, Japan. 16Artificial
Intelligence Research Center, National Instituteof Advanced Industrial Science and Technology,
Tokyo, Japan. 17School of Computer Science,
University of Manchester, Manchester, UK.
18Biomedical Robot Department, Robotics
Division, YASKAWA Electric Corporation,
Kyushu, Japan. 19Database Center for Life
Science (DBCLS), Joint Support-Center for
Data Science Research, Research Organization

312

of Information and Systems (ROIS), Mishima,
Japan. 20MOLCURE, Inc., Tokyo, Japan.
21Department of Biology, Norwegian Universityof Science and Technology, Trondheim, Norway.
22Escuela Técnica Superior de Ingenieros
Informáticos (ETSINF), Universidad Politécnica
de Madrid, Madrid, Spain. 23Chemin deBelleroche 14, Neuchatel, Switzerland.
24Division of Proteomics, Research Center
for Transomics Medicine, Medical Instituteof Bioregulation, Kyushu University, Kyushu,
Japan. 25RIKEN Center for Integrative Medical
Sciences, Kanagawa, Japan. 26Institute forInnovation, Ajinomoto Co., Inc., Kawasaki,
Japan. 27Department of Molecular andCellular Biology, Medical Institute of
Bioregulation, Kyushu University, Kyushu,
Japan. 28Department of Pathophysiology, Schoolof Pharmacy and Pharmaceutical Sciences,
Hoshi University, Tokyo, Japan. 29Divisionof Gene Regulation, Institute for Advanced
Medical Research, School of Medicine, Keio
University, Tokyo, Japan. 30Department of
Systems BioMedicine, Tokyo Medical and Dental
University, Tokyo, Japan. 31Department of
Gastroenterological Surgery, Graduate School of
Medicine, Osaka University, Osaka, Japan.
e-mail: t-natsume@aist.go.jp
Members of the Robotic Biology Consortium
Steering Group: Nozomu Yachie1–5,
Koichi Takahashi3,8,9, Toshiaki Katayama10,
Takeshi Sakurada8, Genki N. Kanda8,9,Eiji Takagi8, Takako Hirose8, Tatsuo Katsura11,
Tetsuo Moriya11, Hiroaki Kitano12–15,
Junichi Tsujii16,17, and Tohru Natsume7,8
Robotics Group: Tomoyuki Shiraki18,
Hirokazu Kariyazaki18, Motohisa Kamei18,
Noriko Abe18, Takuya Fukuda18,
Yukiko Sawada18, Yukio Hashiguchi18,
Kenji Matsukuma8,18, Shinji Murai8,18,
Naoyuki Sasaki8, Tatsuro Ipposhi8,18,

Hideo Urabe8,18, Taku Kudo8,18,
Makoto Umeno18, Seiki Ono18, Kohei
Miyauchi18, Miki Nakamura18, Takahiro
Kizaki18, Takashi Suyama18, Tomohisa Hatta7,8,
and Tohru Natsume7,8
Information Technology Group: Tazro Ohta19,
Koichi Takahashi3,8,9, Yosuke Ozawa8,
Nozomu Yachie1–5, Takeshi Sakurada8,
Kenji Matsukuma8, Shinji Murai8,
Shoji Ihara20, Satoshi Tamaki3,20,
Erick Antezana21, Alexander Garcia-Castro22,
Jean-Luc Perret23, Soh Ishiguro1,3,
Hideto Mori1,3, Daniel Evans-Yamamoto1,3,
Nanami Masuyama1,3, Masaru Tomita3,
Junichi Tsujii16,17, Toshiaki Katayama10, andHiroaki Kitano12–15
Proteomics Group: Tomohisa Hatta7,8,
Masaki Matsumoto24, Hiroshi Nakayama25,
Ayaka Shirasawa26, Kazutaka Shimbo26,
Naoyuki Yamada26, Keiichi I. Nakayama24,27,
and Tohru Natsume7,8
High-Content Cell Screening Group:
Takatsune Shimizu28,29 and Hideyuki Saya29
Epigenetics Group: Satoshi Yamashita30,
Takahide Matsushima30, and Hiroshi
Asahara30
Clinical Group: Hidetoshi Eguchi31,
Manabu Mikamori31, and Masaki Mori31
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With all due respect to Maholo, lab
automation isn’t anthropomorphic
To the Editor:
Biosciences journals often require rigorous
statistical evidence for the data presented
because a substantial proportion of thebiosciences literature is not reproducible1.
In this issue, Yachie et al.2 propose that
LabDroids could form part of a robotic
infrastructure that might contribute toimproved reproducibility. However, we
argue that reproducible research can be
enabled by existing (or new) automation
technologies present in both individual
research groups and centralized DNA
foundries that can be accessed using cloudbased applications.

Most bioscience experiments move small
amounts of liquid from one place to anotherin order to set up experiments and measureeffects. Scientists use tools, such as adjustablemicropipettes, first launched by Eppendorf(Hamburg, Germany) in 1961, to moveliquids in the microliter range. Based on
similar principles, automated technologies
were developed to enable high-throughput
processing. These technologies were mainly
developed to screen large chemical libraries
for drug leads in the pharmaceutical industry.
The Beckman Coulter (Brea, CA, USA; thenSmithKline Coulter) Biomek 1,000-tip-based
liquid handler system was introduced in 1986,

VOLUME 35 NUMBER 4 APRIL 2017 NATURE BIOTECHNOLOGY