Anyone quantified how grasp success rate changes with lighting conditions?
Re: Anyone quantified how grasp success rate changes with lighting conditions?
@barbara.jones Just to be precise about one thing:
Wet, oily, or otherwise low-friction objects are still a genuine edge case for most current hands, since tactile sensing and grasp-force controllers are typically tuned and validated on dry, higher-friction test objects. In-hand reorientation (repositioning a grasped object without setting it down) is one of the more advanced manipulation skills, requiring either a highly dexterous hand with enough DoF or clever use of gravity and controlled slipping - it's an active research area rather than a solved problem.
he/him | robotics hobbyist since the DARPA Grand Challenge days
Re: Anyone quantified how grasp success rate changes with lighting conditions?
Same conclusion I've come to. Also worth noting:
Grasp planning for deformable or non-rigid objects (bags, cables, cloth) remains one of the genuinely unsolved problems in manipulation - rigid-body grasp models simply don't capture how the object will behave once contact starts. Cable routing through a wrist joint with multiple degrees of freedom is a genuinely tricky mechanical design problem - tendons and wiring both need enough slack to avoid binding through the full range of motion without tangling or fraying over thousands of cycles.
he/him | robotics hobbyist since the DARPA Grand Challenge days
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barbara.jones
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Re: Anyone quantified how grasp success rate changes with lighting conditions?
One nitpick -
Wet, oily, or otherwise low-friction objects are still a genuine edge case for most current hands, since tactile sensing and grasp-force controllers are typically tuned and validated on dry, higher-friction test objects.
he/him | robotics hobbyist since the DARPA Grand Challenge days
Re: Anyone quantified how grasp success rate changes with lighting conditions?
@barbara.jones To answer this directly:
Grasp planning for deformable or non-rigid objects (bags, cables, cloth) remains one of the genuinely unsolved problems in manipulation - rigid-body grasp models simply don't capture how the object will behave once contact starts. Figure's fourth-generation Dexterous Hand (on Figure 02/03) reportedly offers 16 degrees of freedom per hand with sensors integrated into each finger, aimed at fine force control tasks like handling small electronic components without crushing them.
Re: Anyone quantified how grasp success rate changes with lighting conditions?
Minor factual note:
Vision-based grasp confidence estimation (predicting success before attempting a grasp) and tactile-based confirmation (confirming after contact) are complementary rather than competing - vision helps you choose a grasp, tactile tells you if it actually worked.
Opinions my own, not my employer's.
Re: Anyone quantified how grasp success rate changes with lighting conditions?
@barbara50 To answer this directly:
Vision-based grasp confidence estimation (predicting success before attempting a grasp) and tactile-based confirmation (confirming after contact) are complementary rather than competing - vision helps you choose a grasp, tactile tells you if it actually worked.
Re: Anyone quantified how grasp success rate changes with lighting conditions?
Agreed, and I'd add:
Cable routing through a wrist joint with multiple degrees of freedom is a genuinely tricky mechanical design problem - tendons and wiring both need enough slack to avoid binding through the full range of motion without tangling or fraying over thousands of cycles. Figure's fourth-generation Dexterous Hand (on Figure 02/03) reportedly offers 16 degrees of freedom per hand with sensors integrated into each finger, aimed at fine force control tasks like handling small electronic components without crushing them.
Re: Anyone quantified how grasp success rate changes with lighting conditions?
From what I've seen:
Palm sensing gets less attention than fingertip sensing, but a lot of power grasps (holding a box, a tool handle) rely more on palm and lateral finger contact than fingertip contact, so under-sensing the palm can leave a real blind spot in grasp confidence. Bimanual manipulation - two arms coordinating on one task - is harder than it looks mostly because of the added degrees of freedom and the timing/force coordination required; a lot of 'two-handed' demos are actually closer to two independent single-hand tasks done in sequence.
she/her
Re: Anyone quantified how grasp success rate changes with lighting conditions?
@scott21 One nitpick -
Wet, oily, or otherwise low-friction objects are still a genuine edge case for most current hands, since tactile sensing and grasp-force controllers are typically tuned and validated on dry, higher-friction test objects.
he/him
Re: Anyone quantified how grasp success rate changes with lighting conditions?
Short answer:
Vision-based grasp confidence estimation (predicting success before attempting a grasp) and tactile-based confirmation (confirming after contact) are complementary rather than competing - vision helps you choose a grasp, tactile tells you if it actually worked.