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ποΈ Iβm currently working on Automotive
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π± Iβm currently learning UI and UX quality test on SW tools for Embedded Systems
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π¬ Ask me about Testing, Optimization, Embedded Systems
π My GitHub RΓ©sumΓ©
just a repo for tests
ποΈ Iβm currently working on Automotive
π± Iβm currently learning UI and UX quality test on SW tools for Embedded Systems
π¬ Ask me about Testing, Optimization, Embedded Systems
π My GitHub RΓ©sumΓ©
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The low level joint control on the arms needs tuning to improve trajectory tracking.
The joint tracking on the arms has up to 4 degrees of error on most joints. This is causing issues with the Cartesian control since the pose of the hands cannot be guaranteed. The right wrist has large errors - up to 20 degrees - though this appears to be a recent problem that we didn't observe a month ago.
We recorded the tracking error for the joints tracking a joint-level trajectory:
We then recorded the joint tracking when running a Cartesian controller. The errors are much larger. Cartesian feedback is used to try and make sure the hands stay together when grasping with 2 hands, but since the low level joint tracking is innacurate this is causing the robot to overcompensate:
Here is a video of the 2-handed grasping before ICRA 2023 and without Cartesian feedback on the position error of the hands. The ergoCub drops the object because the hands do not move together precisely because of the low-level joint tracking problem:
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ergoCub 1.1 S/N:001
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ergoCub 1.0 S/N:000
The low level joint control on the arms needs tuning to improve trajectory tracking.
The joint tracking on the arms has up to 4 degrees of error on most joints. This is causing issues with the Cartesian control since the pose of the hands cannot be guaranteed. The right wrist has large errors - up to 20 degrees - though this appears to be a recent problem that we didn't observe a month ago.
We recorded the tracking error for the joints tracking a joint-level trajectory:
We then recorded the joint tracking when running a Cartesian controller. The errors are much larger. Cartesian feedback is used to try and make sure the hands stay together when grasping with 2 hands, but since the low level joint tracking is innacurate this is causing the robot to overcompensate:
Here is a video of the 2-handed grasping before ICRA 2023 and without Cartesian feedback on the position error of the hands. The ergoCub drops the object because the hands do not move together precisely because of the low-level joint tracking problem:
Here is a video after where feedback on the hand positions was used to try and keep them together. The robot overcompensates because the joints are not tracking accurately and the error on the hand poses increases instead of decreasing:
Myself @Woolfrey and Vignesh @vigisushrutha23 need accurate Cartesian control to demonstrate research in bimanual manipulation as part of the ergoCub project.
High-level feedback control to correct the pose error of the hands is also not possible unless the underlying joint tracking can be guaranteed (as shown in the video).
ergoCub 1.0 S/N:000
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ergoCub 1.0 S/N:000
The low level joint control on the arms needs tuning to improve trajectory tracking.
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ergoCub 1.1 S/N:001
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ergoCub 1.0 S/N:000
ooooookkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
okkkkkkkkkkkkkkkkkkkkkkkkkk
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ddd
dd
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ergoCub 1.0 S/N:000
cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
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ggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg
gggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg
ggggggggggggggggggggggggggg
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iCubBarcelona01 S/N:013
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ddd
ddd
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ergoCub 1.0 S/N:000
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ergoCub 1.0 S/N:000
The low level joint control on the arms needs tuning to improve trajectory tracking.
The joint tracking on the arms has up to 4 degrees of error on most joints. This is causing issues with the Cartesian control since the pose of the hands cannot be guaranteed. The right wrist has large errors - up to 20 degrees - though this appears to be a recent problem that we didn't observe a month ago.
We recorded the tracking error for the joints tracking a joint-level trajectory:
We then recorded the joint tracking when running a Cartesian controller. The errors are much larger. Cartesian feedback is used to try and make sure the hands stay together when grasping with 2 hands, but since the low level joint tracking is innacurate this is causing the robot to overcompensate:
Here is a video of the 2-handed grasping before ICRA 2023 and without Cartesian feedback on the position error of the hands. The ergoCub drops the object because the hands do not move together precisely because of the low-level joint tracking problem:
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ergoCub 1.0 S/N:000
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ergoCub 1.0 S/N:000
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ergoCub 1.0 S/N:000
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iCubErzelli02 S/N:036
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ergoCub 1.0 S/N000
ciao
dw
dwe
dw
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ergoCub 1.0 S/N:000
xxx
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iCubGenova03 S/N:020
aaaaaaaaaaaaaaaaaaaaaaaaaaaaa
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ergoCub 1.0 S/N:000
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iCubBarcelona01 S/N:013
bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb
bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb
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xxx
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iCubBielefeld03 S/N:019
yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy
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iCubBielefeld01 S/N:006
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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ergoCub 1.0 S/N:000
Problema 1,2,3 ...
Degli problema 1:
Dettagli problema 2:
Contesto ulteriore
Urgenza con prioritΓ‘ 2/5.
ergoCub 1.0 S/N:000
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xxx
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iCubGenova03 S/N:020
ooooooooooooooooooooooooooooo
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iCubBarcelona01 S/N:013
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ergoCub 1.0 S/N:000
The low level joint control on the arms needs tuning to improve trajectory tracking.
The joint tracking on the arms has up to 4 degrees of error on most joints. This is causing issues with the Cartesian control since the pose of the hands cannot be guaranteed. The right wrist has large errors - up to 20 degrees - though this appears to be a recent problem that we didn't observe a month ago.
No response
No response
ergoCub 1.0 S/N:000
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ergoCub 1.0 S/N:000
The low level joint control on the arms needs tuning to improve trajectory tracking.
The joint tracking on the arms has up to 4 degrees of error on most joints. This is causing issues with the Cartesian control since the pose of the hands cannot be guaranteed. The right wrist has large errors - up to 20 degrees - though this appears to be a recent problem that we didn't observe a month ago.
We recorded the tracking error for the joints tracking a joint-level trajectory:
We then recorded the joint tracking when running a Cartesian controller. The errors are much larger. Cartesian feedback is used to try and make sure the hands stay together when grasping with 2 hands, but since the low level joint tracking is innacurate this is causing the robot to overcompensate:
Here is a video of the 2-handed grasping before ICRA 2023 and without Cartesian feedback on the position error of the hands. The ergoCub drops the object because the hands do not move together precisely because of the low-level joint tracking problem:
Here is a video after where feedback on the hand positions was used to try and keep them together. The robot overcompensates because the joints are not tracking accurately and the error on the hand poses increases instead of decreasing:
Myself @Woolfrey and Vignesh @vigisushrutha23 need accurate Cartesian control to demonstrate research in bimanual manipulation as part of the ergoCub project.
High-level feedback control to correct the pose error of the hands is also not possible unless the underlying joint tracking can be guaranteed (as shown in the video).
@xEnVrE : FYI
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ccc
cc
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dfghfjgkjhlh
gddh
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iCubBielefeld01 S/N:006
uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu
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ttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt
ttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt
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rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
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ergoCub 1.0 S/N:000
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A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
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