Anyone using simulation-only training for grasp policies with zero real data?
Re: Anyone using simulation-only training for grasp policies with zero real data?
I can speak to this a bit.
A lot of the manipulation shown in production demos still leans heavily on teleoperation, particularly for anything involving fine force control or novel objects - autonomous grasp success rates on genuinely unstructured, previously-unseen clutter are still well below what teleoperation can achieve. 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.
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karen.chen3
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Re: Anyone using simulation-only training for grasp policies with zero real data?
I'd take that specific number with a grain of salt, honestly.
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. 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.
Totally unrelated but has anyone else noticed how fast component costs are dropping this year.
they/them
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diego.moore6
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Re: Anyone using simulation-only training for grasp policies with zero real data?
@karen.chen3 Pretty much this. One thing to add:
Imitation learning from human demonstration video (without robot teleoperation data) is an appealing way to scale up training data cheaply, but it runs into the embodiment gap - human hand kinematics and force profiles don't map directly onto a robot hand's very different mechanism.
Building > buying.
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lisa.gonzalez8
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Re: Anyone using simulation-only training for grasp policies with zero real data?
Appreciate the detailed answer.
Tendon-driven fingers let you move the heavier actuators back into the palm or forearm, keeping the fingers themselves light and fast, but they introduce cable routing, tensioning, and long-term wear problems that direct-actuated fingers don't have.
Kind of makes me think about how different this all looked even three years ago.
she/her | grad student, biped locomotion
Re: Anyone using simulation-only training for grasp policies with zero real data?
Small correction on one detail:
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.
Ex-automotive, now full-time robots.
Re: Anyone using simulation-only training for grasp policies with zero real data?
This raises a question for me -
Compliant wrists that absorb impact during a bad approach or misjudged contact reduce mechanical stress on the whole arm, which matters a lot for long-term reliability even though it's a less visible feature than the hand itself.
Currently: 3D printing my way to bankruptcy.
Re: Anyone using simulation-only training for grasp policies with zero real data?
@priya85 Agreed, and I'd add:
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.
she/her
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sarah.santos3
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Re: Anyone using simulation-only training for grasp policies with zero real data?
@park44 This lines up with my experience.
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.
they/them
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jessica_faro
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Re: Anyone using simulation-only training for grasp policies with zero real data?
@sarah.santos3 I'd push back on this a bit.
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. 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.
Reminds me a bit of the early drone hobbyist scene, honestly.
they/them
Re: Anyone using simulation-only training for grasp policies with zero real data?
Pretty much this. One thing to add:
Underactuated hands (fewer actuators than joints, using mechanical coupling to shape the grasp) are a reasonable engineering compromise for robust power grasps on a budget, but they generally can't do fine in-hand manipulation the way a fully actuated hand can. 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.
Makes me wonder how this looks in another five years.
he/him