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<! Robot Execution Failures
<!----------------------------------------------------------------------->
<HTML> <HEAD>
<TITLE> Robot Execution Failures </TITLE>
</HEAD>
<BODY BGCOLOR="#FFFFFF">
<!------------------------------------------------------------------------
<! Title
<!----------------------------------------------------------------------->
<H1> Robot Execution Failures </H1>
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<! Data Type
<!----------------------------------------------------------------------->
<H2>Data Type</H2>
multivariate time series
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<! Abstract
<!----------------------------------------------------------------------->
<H2>Abstract</H2>
<p>
This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals starting immediately after failure detection.
</p>
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<! Sources
<!----------------------------------------------------------------------->
<H2> Sources</H2>
<H4> Original Owner and Donor</H4>
<PRE>
Luis Seabra Lopes and Luis M. Camarinha-Matos
Universidade Nova de Lisboa,
Monte da Caparica, Portugal
</PRE>
<B>Date Donated: </B> April 23, 1999
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<! Data Characteristics
<!----------------------------------------------------------------------->
<H2> Data Characteristics</H2>
The donation includes 5 datasets, each of them defining a different learning problem:
<UL>
<li> LP1: failures in approach to grasp position </li>
<li> LP2: failures in transfer of a part </li>
<li> LP3: position of part after a transfer failure </li>
<li> LP4: failures in approach to ungrasp position </li>
<li> LP5: failures in motion with part </li>
</UL>
<H4>Feature information </H4>
<P>
All features are numeric although they are integer valued only.
Each feature represents a force or a torque measured after
failure detection; each failure instance is characterized in terms
of 15 force/torque samples collected at regular time intervals
starting immediately after failure detection;
The total observation window for each failure instance was of 315 ms.
</P>
Each example is described as follows:
<PRE>
class
Fx1 Fy1 Fz1 Tx1 Ty1 Tz1
Fx2 Fy2 Fz2 Tx2 Ty2 Tz2
......
Fx15 Fy15 Fz15 Tx15 Ty15 Tz15
</PRE>
<P>
where Fx1 ... Fx15 is the evolution of force Fx in the observation
window, the same for Fy, Fz and the torques; there is a total
of 90 features.
</P>
<H4> Number of instances in each dataset</H4>
<PRE>
-- LP1: 88
-- LP2: 47
-- LP3: 47
-- LP4: 117
-- LP5: 164
</PRE>
<H4>Class Distribution</H4>
<PRE>
-- LP1: 24% normal
19% collision
18% front collision
39% obstruction
-- LP2: 43% normal
13% front collision
15% back collision
11% collision to the right
19% collision to the left
-- LP3: 43% ok
19% slightly moved
32% moved
6% lost
-- LP4: 21% normal
62% collision
18% obstruction
-- LP5: 27% normal
16% bottom collision
13% bottom obstruction
29% collision in part
16% collision in tool
</PRE>
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<! Other Relevant Information
<!----------------------------------------------------------------------->
<H2> Other Relevant Information</H2>
<H4>Feature transformation strategies</H4>
In order to improve classification accuracy, a set of five feature
transformation strategies (based on statistical summary features,
discrete Fourier transform, etc.) was defined and evaluated.
This enabled an average improvement of 20% in accuracy. The most
accessible reference is [Seabra Lopes and Camarinha-Matos, 1998].
<!------------------------------------------------------------------------
<! Data Format
<!----------------------------------------------------------------------->
<H2>Data Format</H2>
The data is stored in five ASCII files, with each example in the files described as follows:
<PRE>
class
Fx1 Fy1 Fz1 Tx1 Ty1 Tz1
Fx2 Fy2 Fz2 Tx2 Ty2 Tz2
......
Fx15 Fy15 Fz15 Tx15 Ty15 Tz15
</PRE>
where Fx1 ... Fx15 is the evolution of force Fx in the observation
window, the same for Fy, Fz and the torques; there is a total
of 90 features.
<!------------------------------------------------------------------------
<! Past Usage
<!----------------------------------------------------------------------->
<H2>Past Usage</H2>
<p>
Seabra Lopes, L. (1997) "Robot Learning at the Task Level:
a Study in the Assembly Domain", Ph.D. thesis, Universidade
Nova de Lisboa, Portugal.
</p>
<p>
Seabra Lopes, L. and L.M. Camarinha-Matos (1998) Feature
Transformation Strategies for a Robot Learning Problem,
"Feature Extraction, Construction and Selection. A Data Mining
Perspective", H. Liu and H. Motoda (edrs.),
Kluwer Academic Publishers.
</p>
<p>
Camarinha-Matos, L.M., L. Seabra Lopes, and J. Barata (1996)
Integration and Learning in Supervision of Flexible Assembly Systems,
"IEEE Transactions on Robotics and Automation", 12 (2), 202-219.
</p>
<!------------------------------------------------------------------------
<! Signature
<!----------------------------------------------------------------------->
<p>
<hr>
<ADDRESS>
<A href="http://kdd.ics.uci.edu/">The UCI KDD Archive</A><br>
<a href="http://www.ics.uci.edu/">Information and Computer Science</a><br>
<a href="http://www.uci.edu/">University of California, Irvine</a><br>
Irvine, CA 92697-3425 <br>
</ADDRESS>
Last modified: March 11, 1999 </BODY>
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