The low level joint control on the arms needs tuning to improve trajectory tracking.
Detailed context
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.
Additional context
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).
Robot Name 🤖
ergoCub 1.0 S/N:000
Request/Failure description
The low level joint control on the arms needs tuning to improve trajectory tracking.
Detailed context
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.
Additional context
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:
https://github.com/robotology/icub-tech-support/assets/62581255/62ec6fc4-258c-4754-a243-615d86ec4164
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:
https://github.com/robotology/icub-tech-support/assets/62581255/3d66a1a1-aed1-476b-9b8c-b29899f4d465
How does it affect you?
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).