An easy way to let a robot execute complex actions is to let the robot copy human moves. Useful information are read by sensors and elaborated to convert them into robot movements. This work focuses on keeping the robot balanced while it is performing an action: grasp an object laying on the ground in front of the robot. Experiments are performed with a human user moving in front of the sensor using a humanoid robot performing the same action, the Vstone Robovie-X.
In this paper we describe a laboratory experience involving a humanoid robot
an its behaviours. This work follows to some similar experiences [
Fig. 1. Overview of the entire system proposed in this paper.
The robot used in this work is a humanoid: the Vstone Robovie-X. RobovieX is a exible and economic platform already used in robotics courses to make ? These authors contributed equally to this work. students familiar with humanoids. Our lab also provided us a simulated version of this humanoid in order to prevent robot damages during the tests.
The remainder of the paper is organized as follows: in Section 2 the data acquisition system is explained, while the robot structure is illustrated in Section 3. The stability algorithms used, math and physics behind them and some speci c tests performed are described in Section 4. Finally, Section 5 contains conclusions. 2
In this paper, data are acquired from a low cost RGB-D sensor: a person can
perform the desired actions in front of the sensor without any need of additional
equipment. A skeleton tracking system is used to extract human joints
positions and angles from the raw data provided by the sensor, namely a Microsoft
Kinect. The skeleton information (Figure 2) is subsequently remapped to the
robot model, so that the person acting in front of the camera could simply
teleoperate the robot by his own body, similarly to the systems described in [
A consolidated method to maintain the robot stability [
At each instant, the CoM is equal to:
N
P mk xk CoMx = k=1
; M
N
P mk yk CoMy = k=1
; M
N
P mk zk CoMz = k=1
M (1) where N = 17 is the number of joints, mk is the kth joint inertial mass, M is the mass of the robot, and xk; yk; zk are the coordinates of the kth joint with respect to the Torso joint.
Thus, the ground projection of the CoM is given by (CoMx; CoMy), and it has to satisfy the constraints 90cm < Comx < 90cm and 35:5cm < Comy < 35:5cm that is the area covered by the robot foot in the initial standing position. The selected movement is quite simple, so several solutions are feasible to solve the problem. We imposed a strong relation between hip, knee an ankle joints in order to involve all the lower body joints and at the same time adapt to the human natural behaviour.
= = 2 ; 0 157o (2) where is the pitch hip angle at instant t, is the knee angle at instant t, and is the pitch ankle angle at instant t. Fig. 4 shows the three described angles in the robot model. The method was tested experimentally on both simulated and real robot. 4.2
Re nement We re ne the described technique applying di erent strategies in order to avoid some rough robot movements we noticed during the initial tests. Studying the dynamics of the task, we decided to limit roll movements (i.e. lateral movements) in joints not involved in reaching the goal, like hips and ankles.
Fast and sudden movements could threaten the robot stability while performing an activity. Ensuring the smoothness of all robot moves is essential in balancing purpose. Without any focused control, the input data can make the robot move jerkily. This problem is due to the fast human movements compared with the frame rate of the sensor and to the margin of error of the skeleton tracker computing joints positions.
The data acquired by the RGB-D sensor are ltered to remove the noise by calculating the mean value of the last three angle values of every joint in order to avoid rough robot movements and obtain a smoother motion.
The pose feedback equation is: where t is the raw value given by the skeleton tracker for a certain joint, ^t is the computed value at the instant t for the considered joint, ^t 1 is the computed value at the instant t 1 for the considered joint, ^t 2 is the computed value at the instant t 2 for the considered joint.
Moreover, the movements of the right and left side of the robot body are coordinated in order to make easier for the robot to grasp the object. In this way, we also increase the robot stability and its precision during the motion.
Finally, the proportions between lower body angles are computed according to the equation 2. These re ned data are used as input to the algorithm checking if the CoM projection on the ground is inside the stability area.
Again, the whole system has been tested with many users and di erent objects1 on both real and simulated environment. The applied re nements significantly improved the performance and the users easily reached the goal. It is worth to notice that a slight delay is introduced by USB connection between the system and the real robot, nevertheless no delay is present in the simulated model, that works at 30 fps. We also can make real and simulated robot work together, so humans can take advantage of the information provided by the virtual model. 5
In this paper, a robot behaviour was developed in order to maintain robot stability during a pinking up task performed by human teleoperation. The system keeps the robot stable using movements as similar as possible to the user ones. Our technique reach real-time performances, the robot is able to move smoothly according to user movements.
The system has been tested with di erent users and objects and it has also been exposed as a working demo to the \The Researchers Night"2 in Padova.
The work described in this paper could be used jointly with a Robot Learning
from Demonstration (RLfD) framework already developed in our lab [
As future work we also will improve the system by increasing the set of supported actions and applying it to scenarios involving innovative robotic platforms like exoskeletons.