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首页2016上海IAS-14:智能自治系统会议论文集,探索机器人与工业自主革命
2016上海IAS-14:智能自治系统会议论文集,探索机器人与工业自主革命
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"《智能自治系统14》是一本收录于2017年2月11日的会议论文集,由Intelligent Systems and Computing系列的一部分,第531卷出版。该书聚焦于第14届国际智能自治系统会议(IAS-14),这是一场自1986年起每两年举办一次的机器人领域盛事,旨在展示世界顶尖研究者、工程师和实践者关于机器人技术的最新进展、创新和未来愿景。会议在中国上海举行,于2016年7月召开。 书中汇集了关于机器人领域的重要研究成果,特别是自主机器人和智能系统的前沿发展,强调了它们在工业生产中的崭新角色,以及其在移动、操作、感知、推理和决策等能力上的显著提升。这些论文经过同行评审,反映了该领域的成熟度和稳健性,包含了众多令人兴奋和富有远见的思想,将推动科研界探索新的研究方向。 "Advances in Intelligent Systems and Computing" 这个系列书籍覆盖了广泛的学科领域,包括工程、自然科学、计算机科学、信息技术、经济、商业、电子商务、环境、健康护理、生命科学等,旨在分享智能系统和智能计算的理论、应用和设计方法的最新成果。《智能自治系统14》作为系列的一部分,不仅包含教科书,也记录了该领域内关键会议、研讨会和大会的精华内容,深入探讨了基础研究和实际应用的结合。 通过阅读这本书,读者可以深入了解当前智能自主系统的技术前沿,以及它们如何改变和影响各行各业的实践,为未来的技术创新和产业发展提供了宝贵的洞见。对于机器人爱好者、研究人员、工程师以及相关行业从业者来说,这是一份不可多得的参考资料。"
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Part I
Embodied-Brain Systems Science
![](https://csdnimg.cn/release/download_crawler_static/9962088/bg11.jpg)
Clustering Latent Sensor Distribution
on Body Map for Generating Body Schema
Tomohiro Mimura, Yoshinobu Hagiwara,
Tadahiro Taniguchi and Tetsunari Inamura
Abstract A computational theory model is constructed to generate a body map and
a body schema. It is assumed that the fetus has a body map and a body schema to
control and understand its body and generates them based on tactile information
obtained by random motion in the womb called general movement. As a first step, in
this research, a computational theory model is proposed to generate the body Map
based on tactile information and to estimate nodes of the body schema by machine
learning methods.
Keywords Body map
⋅ Body schema ⋅ Clustering
1 Introduction
Human beings can unconsciously control their bodies based on a musculoskeletal
system that is constructed by integrating multimodal information in their brains [1].
Human beings also can understand surrounding environments through their bodies,
because they have a topological map in their brains that represents the position of
each recipient cell in their bodies [2, 3]. In this study, we assume that human beings
have two types of models in their brains: a multilink system called a body schema
T. Mimura (
✉
) ⋅ Y. Hagiwara (
✉
) ⋅ T. Taniguchi (
✉
)
Ritsumeikan University, 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577, Japan
e-mail: t.mimura@em.ci.ritsumei.ac.jp
Y. H agi wara
e-mail: yhagiwara@em.ci.ritsumei.ac.jp
T. Taniguchi
e-mail: taniguchi@em.ci.ritsumei.ac.jp
T. Inamura (
✉
)
National Institute of Informatics, 2-1-2 Hitotsubashi,
Chiyoda-ku, Tokyo 101-8430, Japan
e-mail: inamura@nii.ac.jp
© Springer International Publishing AG 2017
W. Chen et al. (eds.), Intelligent Autonomous Systems 14,
Advances in Intelligent Systems and Computing 531,
DOI 10.1007/978-3-319-48036-7_1
3
![](https://csdnimg.cn/release/download_crawler_static/9962088/bg12.jpg)
4 T. Mimura et al.
Fig. 1 A body map and a body schema in the human brain
used to control their bodies and a topological map called a body map used to under-
stand the surrounding environment through their bodies as shown in Fig. 1.
In previous research, Prechtl et al. reported that a fetus obtains a body schema
through random motion in the womb called “general movement” [4]. General move-
ment is defined as spontaneous activity by the fetus manifested as a smooth and
random whole-body motion observed from 8.5 to 9.5 weeks. It is assumed that the
fetus generates a body schema based on the relationship between motion and tac-
tile information obtained by general movement because neighboring recipient-cells
return similar tactile information owing to the high viscosity of amniotic fluid.
In this study, we aim to construct a computational model that generates a body
map and a body schema based on tactile information obtained by random motion,
i.e., general movement.
The computational model generates a body map by using multidimensional scal-
ing (MDS) [5] and information metric based on tactile information, and estimate
nodes of a body schema by using Dirichlet process Gaussian mixture model
(DPGMM) [6] from the generated body map.
2 Previous Research
2.1 Body Map
Human beings have body maps that are topological maps for their bodies in the
brain [2]. Like an exhaustive patchwork body maps cover not only external body
parts such as fingers, shoulders, elbows, and so on but also viscera.
In a previous study of body maps, Olsson et al. proposed a method to calculate
an information measure based on the information of sensors attached to the robot
AIBO and to replace each sensor with a two-dimensional map based on the calculated
information measure [7]. However, this study is not intended for estimation of a
multilink system.
![](https://csdnimg.cn/release/download_crawler_static/9962088/bg13.jpg)
Clustering Latent Sensor Distribution on Body Map . .. 5
Mori et al. provided a constructive model of a human fetus that forms behaviors
and understanding regarding its body through general movements in the womb of
its mother [8]. Sugiura et al. solved a similar problem by using the time difference
of arrival of signals and multidimensional scaling [9]. Kuniyoshi et al. generated
topographic somatosensory cluster map that is plotted the fetus’s sensor points on a
2 dimensional space as a result of a self-organizing process [10].
2.2 Body Schema
Even though the human body has multiple degrees of freedom, human beings can
control their bodies without considering the angle of joints and the length of links.
It is assumed that a body schema enables human beings to unconsciously control
their bodies. Humans have a multilink system in their brains for understanding their
bodies. Henry et al. define body schema as a unconscious process that dynamically
works when human beings control their motion and pose [11].
In a previous study of body schema, Martinez et al. estimated a multilink sys-
tem using an active-learning method based on vision information [12]. Nabeshima
et al. proposed a model that enables robots to extend their body schema, when robots
use tools, based on multimodal information. It is difficult to apply these methods to
our study which to estimate body map and body schema based on tactile information
because these methods require multimodal information including vision information.
3 Proposed Method
3.1 Overview
Figure 2 shows an approach of this study used to generate a body map and a body
schema. The proposed method consists of the following three steps. First, a topolog-
ical map is generated based on tactile information obtained from receptor cells on
the embodied system, as shown in Fig. 2a. Second, nodes of body schema are esti-
mated by clustering the generated topological map, as shown in Fig. 2b. The third
step entails generating a tree structure based on tactile information and the estimated
nodes of the body schema, as shown in Fig. 2c. As a preliminary stage to generate a
body schema, our approach generates a tree structure instead of a multilink system.
In this paper, we propose a method to generate a body map and a method to estimate
the nodes of the body schema, as shown in Fig. 2a and b, respectively.
![](https://csdnimg.cn/release/download_crawler_static/9962088/bg14.jpg)
6 T. Mimura et al.
Fig. 2 Overview of the proposed approach. a Body map generation. b Node estimation. c Body
schema generation
3.2 Generation of the Body Map
To generate a body map, we used the summation of conditional entropy and multi-
dimensional scaling (MDS) [5]. Olsson et al. defined the summation of conditional
entropy as an information metric between sensors [13]. The information metric d
satisfies the following metric space axioms:
D(X, Y)=D(Y, X) (Symmetry)
D(X, Y)=0 iff Y = X (Equivalence)
D(X, Z) ≤ D(X, Y)+D(Y, Z) (Triangle Inequality),
when the model has M tactile sensors, the information metric d(S
i
, S
j
) between two
tactile sensors i and j is calculated from the equation:
D(S
i
, S
j
)=H(S
i
|S
j
)+H(S
j
|S
i
), (1)
where S
i
and S
j
are sets of tactile data at two sensors i and j, respectively. The con-
ditional entropy H(S
i
|S
j
) is defined by the equation:
H(S
j
|S
i
)∶=
∑
x∈S
i
p(x)H(S
j
|S
i
= x)
∶= −
∑
x∈S
i
∑
y∈S
j
p(x, y)log
2
p(y|x), (2)
where x and y are tactile data and elements of S
i
and S
j
, respectively.
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