@copilot, following up on #3 (comment), help me understand the current visual servoing implementation. I'm unsure if the implementation is on main or not, but it seems more likely that it's on apriltag-implementation branch

EDIT: cc @kelvinchow23 (lmk if I'm missing the most recent code you're working on)

@copilot, following up on #3 (comment), help me understand the current visual servoing implementation. I'm unsure if the implem...

The visual servoing implementation is present on both branches:

Current status (as of 230a747):

• Main branch: Contains full visual servoing implementation (src/ur_toolkit/visual_servo/)
• apriltag-implementation branch: Also contains the same visual servoing code (last updated 9d4a1e5)

The visual servoing system includes:

• visual_servo_engine.py - Eye-in-hand IBVS implementation with PID control
• detection_filter.py - Detection quality filtering
• pose_history.py - Pose tracking and history management
• config.py - Visual servo configuration management

Both branches have identical visual servo functionality. The implementation includes a complete Eye-in-Hand Visual Servoing system with PID controllers, AprilTag integration, and workflow integration as documented in my review.

@copilot what might be the issue?

currently trying to get visual servoing (or some form of correction robot pose basde on apriltag pose estimation information). There's a position error (rotation + translation) that it spits out, but it cant seem to converge to the corrected position.

@copilot what might be the issue?

currently trying to get visual servoing (or some form of correction robot pose basde on apriltag pose estimation information). There's a position error (rotation...

Several factors could cause visual servoing convergence issues. Based on the implementation analysis:

Primary Potential Issues:

1. Control Loop Parameters

The PID gains are very conservative (lines 109-118):

# Translation: kp=0.3, ki=0.0, kd=0.0, output_limit=0.015m
# Rotation: kp=0.2, ki=0.0, kd=0.0, output_limit=0.05rad

Problem: Low gains + no integral term = slow convergence, may never reach tight tolerances

2. Tolerances vs. Control Authority

• Position tolerance: 2mm (line 36)
• Max translation correction: 15mm/iteration (line 109)
• But with 70% damping factor (line 61), effective correction = ~10mm/iteration

Problem: Small corrections may not overcome system noise/deadband

3. Error Calculation Method

Simple direct correction (lines 297-298):

robot_translation_correction = -tag_translation_error
robot_rotation_correction = -tag_rotation_error  

Problem: Assumes perfect camera-robot frame alignment; doesn't account for:

• Camera mounting errors
• Robot kinematic errors
• Coordinate frame transformations

4. Detection Inconsistency Checks Disabled

Lines 262-278 show detection consistency checks are commented out for testing.
Problem: May be applying corrections based on noisy measurements

Troubleshooting Steps:

1. Check detection quality: Review Hamming distance and decision margin from AprilTag detections
2. Increase damping: Try damping_factor: 0.3-0.5 instead of 0.7
3. Add integral term: Set ki=0.05 for translation controllers to eliminate steady-state error
4. Relax tolerances: Try position_tolerance: 0.005 (5mm) initially
5. Enable debug logging: Add error magnitude tracking between iterations

The system appears designed for stability over performance. For better convergence, consider increasing control gains gradually while monitoring for oscillation.

@kelvinchow23 could you include a video of running one of the workflows? (and also include a permalink to the code that you're running)

@copilot fetch https://chatgpt.com/share/68ddddf5-d048-8007-bc6d-2a3fbc9794d5. In light of this, do your recommendations still apply?

@copilot fetch https://chatgpt.com/share/68ddddf5-d048-8007-bc6d-2a3fbc9794d5. In light of this, do your recommendations still apply?

I'm unable to access the ChatGPT conversation link (blocked by client). Could you share the key insights or findings from that conversation? This will help me determine if my previous recommendations about the visual servoing convergence issues need to be revised.

@copilot I added copy-paste of transcript as a txt file

@copilot I added copy-paste of transcript as a txt file

After reviewing the transcript, my recommendations remain valid with additional context:

Key Insights from Transcript:

The conversation confirms this is Position-Based Visual Servoing (PBVS) which:

• Uses 6-DoF pose error in SE(3) space
• Maps error to joint velocities via robot Jacobian
• Should run at 20-60 Hz (camera rate)
• Is "gradient descent with a map" - structured, not blind search

How This Relates to Convergence Issues:

1. Missing Jacobian Usage

The current implementation (lines 297-298) does direct pose correction:

robot_correction = -tag_error

Problem: This assumes identity Jacobian mapping, which is rarely correct. True PBVS needs:

joint_velocity = J_pseudoinverse @ (-lambda * pose_error)

2. Control Rate Mismatch

Transcript recommends 20-60 Hz control loop. Current implementation likely runs much slower (depends on camera capture + detection filtering).

3. No Velocity-Based Control

Current system sends position commands, not velocity commands. PBVS theory assumes continuous velocity control at camera rate.

Updated Recommendations:

For proper PBVS convergence:

1. Implement Jacobian-based control (requires robot kinematics)
2. Increase control loop frequency to match camera rate
3. Switch to velocity commands instead of position steps
4. Use smaller gain (λ=0.1-0.5) with proper Jacobian

For current simplified approach:

• My original recommendations still apply (tune gains, add integral term, relax tolerances)
• Consider this is approximating PBVS without full kinematic model
• May need hand-eye calibration for better frame alignment

@copilot Run it and commit new file(s)

@copilot You seem to have gotten into a loop trying to run that file. Try again. I want you to commit the updated gif.

@copilot GIF still showing 150 iterations. Nvm, will come back to this later.

Here's a video of an initial to final with moveL using hard-coded coordinates and no AprilTag / visual servoing

From here, the idea is to see if we can accomplish the same procedure using visual servoing

Fun poking around with the robot with you. Just me, or does it seem like we don't really need visual servoing with a UR arm? I.e., the UR's moveL commands and the inverse kinematics are good enough that we really just need the single step to get there (?)

In some sense, the visual servoing is just a way to intelligently run multiple iterations of AprilTag based move commands. Maybe the lesson here is that we could use a single move command based on an AprilTag to get 99% of the way there, and then we do a bit of visual servoing to self-correct and get it to exactly the position we wanted?

Fun poking around with the robot with you. Just me, or does it seem like we don't really need visual servoing with a UR arm? I.e., the UR's moveL commands and the inverse kinematics are good enough that we really just need the single step to get there (?)

In some sense, the visual servoing is just a way to intelligently run multiple iterations of AprilTag based move commands. Maybe the lesson here is that we could use a single move command based on an AprilTag to get 99% of the way there, and then we do a bit of visual servoing to self-correct and get it to exactly the position we wanted?

i think this still needs work, it always hits the robot limit and locks up, then moves directly to the end hardcoded position. even when i changed the poses so they were a few centimetres next to each other, it would still go in a direction (and keep going) until it locks up.

i was mis-using the term visual servoing. that's not really what i was doing previously. it was

1. take snapshot of tag in the camera frame.
2. compare with saved tag location
3. compute error
4. apply transformation to calculate new robot arm joints/tcp
5. move arm to minimize error
6. repeat

however, it wasnt working. the error didnt converge. this visual servoing approach i dont think converges either.

reading a bit more about it, i want to try to see if it was apriltag ambiguity. i'm going to provide some log data so it can be interpreted to see if this was the case.