Proceedings of the 2015
IEEE Conference on Robotics and Biomimetics
Zhuhai, China, December 6-9, 2015

Sparsely Extracting Stored Movements
to Construct Interfaces for Humanoid End-effector Control
Yuka Ariki1 , Tetsunari Inamura2 , Shiro Ikeda3 and Jun Morimoto4
Abstract— This paper proposes a robot interface design
method by which we can control humanoid end-effector movements with such a low-dimensional input device as a gamepad.
In our proposed method, first, the numbers of movement trajectories to accomplish different tasks are generated using a simulated robot model and stored in a database. Second, a humanuser demonstrates the current task-related behavior. Third, thecorresponding stored movements for the demonstrated humanbehavior are sparsely extracted by a sparse coding method.
Finally, the sparsely extracted movement bases are linearly
combined to generate a novel movement to accomplish a newtarget task where the linear weight parameters are modulatedby the gamepad. We easily generated such complicated hand
movements as spiral motions on a small humanoid robot with
our proposed interface.

I. INTRODUCTION
As robotic technologies continue to progress, robots are
expected to support daily human activities, engage in manufacturing, and assist disaster-recovery efforts. To use robotic
devices for supporting human activities, we must properly
design an interface that connects human users and robots.
Using vision systems to monitor human gestures for recognizing user intentions is a popular approach [1], [2], [3]. A
hand movement recognition device such as a Wii controller
has also been used as a robot interface [4]. Measuring
myoelectric signals is another promising approach to design
an intuitive interface to control robotic devices [5], [6],[7]. Brain machine interfaces are also getting attention as
a potentially very useful robot control interface [8], [9].
On the other hand, one of the most popular interfaces tocontrol high-dimensional systems is a gamepad. Using sucha low-dimensional input device as a gamepad to control highdimensional systems, including in-game characters, is a very
common interface implementation.
Interestingly, users do not need to spend a significant
amount of time for training to achieve a certain skill levelto properly control the in-game characters. This is probably
because the design that connects the input device to the highdimensional system is proper. The development of an proper
1 Y.

Ariki is with Dept. of Science and Engineering,
Ritsumeikan University, 1-1-1 Kusatsu NoziHigashi, Shiga, Japanyayukaar@fc.ritsumei.ac.jp
2 T. Inamura with National Institute of Informatics, The Graduate School
for Advanced Studies, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japaninamura@nii.ac.jp
3 S. Ikeda with The Institute of Statistical Mathematics, 10-3 Midori-cho,Tachikawa, Tokyo 190-8562, Japan shiro@ism.ac.jp
4 J. Morimoto is with Dept. of Brain Robot Interface, ATR Computational
Neuroscience Laboratories, 2-2-2 Hikaridai Seikacho Sorakugun, Kyoto,
Japan xmorimo@atr.jp

978-1-4673-9674-5/15/$31.00 © 2015 IEEE

robot interface, which allows users to control a complicatedrobotic device without significant effort, is critical.
To develop such a useful robot interface, we need tofind proper constraints to connect low-dimensional input
devices with a robot system. In previous studies, manifold
learning methods, e.g., [10], [11], have been used to findlow-dimensional representations of high-dimensional human
movements as proper constraints [12], [13], [14], [15], [16],[17]. However, in these previous approaches, since an appropriate low-dimensional manifold needs to be extracted foreach target behavior, they are not suitable for generating awide variety of movements.
On the other hand, task-space control is a standard approach to generate high-dimensional whole-body humanoid
movements that correspond to task-relevant end-effector trajectories [18], [19], [20]. However, it is not very easy togenerate three-dimensional task-space hand movements by
a gamepad when users need to generate complicated hand
trajectories. In addition, since users need to continuously
control a humanoid end-effector using the input device,generating fast movements is also difficult.
In this paper, we consider a different approach for a robot
interface design. We adopt the ideas of movement primitives [21] or motor tapes [22] to generate robot movements.
Previous studies showed that these approaches are useful for
generating a variety of movements from previously learned
motions to adapt to the surrounding environments and thegiven tasks [23], [22]. In our approach, we first store different trajectories as basis movements that can be considered
movement primitives. Then we use a sparse coding approachby which a newly observed movement is represented withsparsely selected basis trajectories. By linearly combining
the selected basis trajectories, users can generate different
types of movements, where the combinations of the bases isdetermined using a low-dimensional input device such as a
gamepad.
This paper is organized as follows. In Section II, we
explain our strategy for constructing a low-dimensional interface for humanoid end-effector control with a sparse coding
approach. In Section III, we introduce our experimental
setups that are composed of a small humanoid robot platform
and a low-dimensional input device. In Section IV, we show
our experimental results using the simulations and a realhumanoid robot. We also present control performances witha designed humanoid interface, which we compare with astandard task-space control interface.

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Fig. 1. Schematic diagram of our proposed framework. In our proposed method, 1) numbers of movement trajectories to accomplish different tasks
are generated with a simulated robot model and 2) stored in a database; 3) human user demonstrates current task-related behavior; 4), 5) corresponding
stored movements and parameters to demonstrated behavior are sparsely extracted by a sparse coding method; 6), 7) sparsely extracted movement bases
are linearly combined to generate novel movement to accomplish a new target task where linear weights for combinations are controlled by gamepad.

II. M ETHODS
Here we introduce our interface design method for humanoid end-effector control. Figure 1 shows the interface
design strategy. First, we present how we store basis movements into a database by using a simulated environment.
Second, we introduce how to extract basis movement trajectories from the stored movements. Finally, we describe
how to generate humanoid end-effector movements by using
a low-dimensional input device such as a gamepad based on
the extracted bases.
A. Storing movement bases
We first store numbers of end-effector movement trajectories in a movement database by using a simulated humanoid
robot model. It is easy to design wide variety of movements
for accomplish different tasks in a simulated environment.
We then solve inverse kinematics problems to generatecorresponding joint movements when we control a humanoid
platform. A simulated robot generates numbers of taskrelated hand movements and these end-effector trajectories
are stored as movement bases. Here we assume that we have
N trajectory bases, where each end-effector trajectory basis
has m sample points. Then, the movement basis storage canbe represented as:
D = [b1 , b2 , . . . , bN ] ,where bi ∈ Rmsparse coding method based on L1 -norm regularization [24].
Concretely, we solve a sparse linear regression problem:x∗ = argminx ||y − Dx||22 + λ||x||1 ,(2)

where x denotes weight parameter vector and y denotes anobserved hand-movement trajectory of a human demonstrator. Sparsity of the solution depends on the parameter λ. x∗
denotes optimized weight parameter.
C. Generating movements by constructed interface
Finally, we generate task-related end-effector movementsby controlling the weight parameters that correspond to theextracted basis trajectories. To control the weight parameters,
we use low-dimensional input device such as a gamepad. By
using the modified weight parameters, end-effector movement trajectories are represented as:whereỹ = Dx̃,(3)x̃ = x∗ + ∆x

(4)denotes the modified input parameter vector composed ofthe parameter find in the reconstruction process x∗ in (2)and the low-dimensional control input ∆x. ỹ denotes the
newly generated robot movement trajectory.

(1)
III. E XPERIMENTAL SETUPS

denotes each trajectory basis.

B. Extracting movement bases

A. Small humanoid robot platform and Low-dimensional
input device

We then linearly combine end-effector trajectory basis,
where the trajectories are sparsely extracted from the movement database. For selecting these basis, we consider using a

We use a 25-DOF small humanoid platform (see also
Fig. 2(A)) to evaluate our proposed interface design method.
As an input device, we use a gamepad which has two of

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Fig. 3.

An example of the stored spiral trajectories that are used in our experiment, and corresponding robot postures.

corresponding robot postures.
C. Spiral movement demonstration

Fig. 2. (A) 25-DOF small humanoid platform, (B) Low-dimensional input
device. The device has two of two-DOF analog joysticks.

two-DOF analog joysticks (see also Fig. 2(B)). The analog
joystick inputs were used to determine the parameter inputs∆x in (4). Then, a trajectory composed of the linearly
combined bases was used to control the small humanoid
platform.

From the constructed movement database, we sparsely extracted four movement bases by observing a hand movement
y demonstrated by a human user. As examples, a humanuser demonstrated three different types of spiral motions as
depicted in Figs. 4(A),(D) and (G). Then, for each observed
spiral movement, corresponding trajectories in the movement
database are sparsely selected by the basis selection method
presented in (2). The four movement bases are selectedbased on the absolute value of the element of the optimized
parameter vector x∗ in (2). Note that we used lambda
parameter λ = 0.001 so that less than ten movement bases
were sparsely selected from the database which includes 96
bases. In other words, based on the absolute value of theelement of the optimized parameter vector, we finally chosethe four bases from the less than ten bases selected by thesparse coding method.

B. Constructing spiral movement database
We constructed end-effector movement database using
a simulation environment of the small humanoid platform
depicted in Fig. 2(A). In this study, we consider generatingcomplicated hand movements such as spiral trajectories. In
our experiment, 96 spiral trajectories are generated in thesimulated environments, where the spirals with four different
sizes, three different length, and eight different rotation
angles were considered, i.e., 4 × 3 × 8 = 96. Then, thegenerated trajectories were stored and represented in a matrix
from D in (1). Figure 3 shows an example of the stored
spiral trajectories that were used in our experiment, and

D. Spiral movement generation
The parameters for the selected basis trajectories arecontrolled to generate modified spiral movements. These
parameters were controlled by using the gamepad (see also
Fig.2(B)). The two of the two-DOF joysticks were assigned
to control the selected four parameters. We show that thesmall humanoid platform can generate similar but different
spiral hand movements from the observed spiral trajectories
by controlling parameters with using the gamepad. We thenalso show that we can control the small humanoid robot togenerate the modulated spiral movement.

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Fig. 4. Generated spiral movements. (A)-(C) correspond to first observed movement, (D)-(F) correspond to second observed movement, (G)-(I) correspondto third observed movement. (A),(D),(G): Demonstrated and reconstructed spirals. Demonstrated movements were measured by using Visualeyez real-time
motion capture system (Phoenix Technologies Inc.). (B),(E),(H): Selected basis end-effector trajectories. (C),(F),(I): Modulated spirals by using proposedinterface.

IV. R ESULTS
A. Generating spiral movements
We first showed that we were able to generate spiral movements by using the low-dimensional input device. Figure 4
shows the results of the interface construction procedure.
Human user demonstrated different spiral movements thathave three different shapes (see also Figs. 4 (A),(D) and (G)).
For each demonstrated movement, the four movement bases
were selected by using (2) (see also Figs. 4 (B),(E) and (H))
and the corresponding parameters were controlled by using
the gamepad. In Figs. 4(C),(F) and (I), modulated spirals
are presented for each different demonstrated movement. By
using the constructed robot interface, we showed that theuser was able to easily control the end effector of the small
humanoid robot by using the gamepad input device based on
the sparsely selected basis trajectories. Figure 5 shows thereal humanoid robot movement when the robot generated themodulated spiral movement which is presented in Fig.4(C).
B. Spiral movement control to generate a desired trajectory
We then showed that the constructed humanoid interface
were able to be used to follow a target spiral movement bycontrolling the small humanoid robot with using the gamepad
input device. Although generating a spiral movement byusing the constructed robot interface was easy, still, sometraining trials were necessary for a user to follow the target
spirals. We show the learning performance of a user togenerate a target spiral movement in Fig. 6. Within 30
trials, humanoid end-effector control performance was much
improved, where each trial only takes around one second.
As a comparison, we also asked the same subject to generate a target spiral movement by using an control interface
by which the hand position in a cartesian coordinate weredirectory controlled by the two-DOF analog joystick. Figure
7 shows the comparison of the generated spiral movements.
As in Figure 7(B), direct control of the hand position forgenerating the spiral trajectory by using the gamepad wasvery difficult while, as in Fig.7(A), it was easy to generate
spiral end-effector movement to follow the target trajectoryby using the proposed interface. These results clearly showthat our proposed approach was much easier to be used
to generate a complicated end-effector trajectory such as aspiral movement.
V. CONCLUSIONSWe proposed a humanoid interface design method. We
showed that we can easily generate spiral hand movements
on a small humanoid platform by using the gamepad input
device. Humanoid robot movements were controlled by the1819

Fig. 5.

Generated spiral movements on real humanoid platform. Red disks represent end-effector positions.

Fig. 6. Spiral control performance using the proposed interface. Within
30 trials, humanoid end-effector control performance was much improved,
where each trial only takes around one second. Mean and standard deviationof errors for every five trials are plotted.

combined basis motion trajectories, where the parameters to
combine each basis were determined by the input commandspecified with the gamepad. The basis trajectories weresparsely extracted from the stored spiral trajectories by using
the sparse coding method. Different spiral movements weresuccessfully generated by using the constructed interface.
Since the stored basis trajectories were generated in the simulated humanoid robot system, it would not be always suitable
to code the observed human movements. In such case,
we can possibly use a dictionary learning method [25] toadapt the stored basis motion to observed human movement
trajectories. Therefore, in our future study, we consider using
this kind of adaptation mechanism to refine the stored basis
trajectories to properly represent observed human behaviors.
ACKNOWLEDGMENT
This is the results carried out under MIC-SCOPE. Part ofthis research was supported by SRPBS, AMED/MEXT. This
research was also partially supported by MEXT KAKENHI
grant Number 23120004, ImPACT Program of Council for
Science, Technology and Innovation, and NEDO.
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Fig. 7. Comparison of the generated spiral movements. This results clearlyshow that our proposed approach was much easier to be used to generate acomplicated end-effector movement such as a spiral trajectory. (A)Proposed.
(B)Control interface by which the hand position in a Cartesian coordinate
were directory controlled by the two-DOF analog joystick.

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