SketchMate: Agentic Geometric Reasoning for Zero-Shot Precision Insertion
Overview
This research introduces SketchMate, a system that uses vision-language models to create geometric sketches of insertion tasks. By decoupling part description from placement and using sketch memory with point-cloud registration, the method achieves sub-millimetre precision in simulated environments. On 69 episodes, it successfully inserted 88% of unseen parts at the correct pose, significantly outperforming existing zero-shot agentic manipulation methods which failed on the same task.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“asping the part, the robot inspects both mating faces at close range and corrects the sketch by shape matching. On 69 simulated insertion episodes, our method inserts 88% of the parts at the assembled pose in a strict kinematic test, while ReKep and MOKA with the same VLM insert none. On new placements of eight tasks, reusing one stored run inserts as many parts as calling the agent in each episode, and it produces t”
Exact source · revision 1Source owner not reported
Reported quantity · sketch generation time: 3.4 other · Basis not reported
“he sketch in 3.4 s instead of about 90 s.”
Exact source · revision 1Source owner not reported
Reported quantity · baseline sketch generation time: 90 other · Basis not reported
“he sketch in 3.4 s instead of about 90 s.”
Exact source · revision 1Source owner not reported
Reported quantity · simulated episodes: 69 other · reported trials
“asping the part, the robot inspects both mating faces at close range and corrects the sketch by shape matching. On 69 simulated insertion episodes, our method inserts 88% of the parts at the assembled pose in a strict kinematic test, while ReKep and MOKA with the same VLM insert none. On new placements of eight tasks, reusing one stored run inserts as many parts as calling the agent in each episode, and it produces t”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks: 8 other · Basis not reported
“asping the part, the robot inspects both mating faces at close range and corrects the sketch by shape matching. On 69 simulated insertion episodes, our method inserts 88% of the parts at the assembled pose in a strict kinematic test, while ReKep and MOKA with the same VLM insert none. On new placements of eight tasks, reusing one stored run inserts as many parts as calling the agent in each episode, and it produces t”
Exact source · revision 1Source owner not reported
Reported quantity · robots: 1 other · Basis not reported
“asping the part, the robot inspects both mating faces at close range and corrects the sketch by shape matching. On 69 simulated insertion episodes, our method inserts 88% of the parts at the assembled pose in a strict kinematic test, while ReKep and MOKA with the same VLM insert none. On new placements of eight tasks, reusing one stored run inserts as many parts as calling the agent in each episode, and it produces t”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSketchMate: Agentic Geometric Reasoning for Zero-Shot Precision Insertion
This research introduces SketchMate, a system that uses vision-language models to create geometric sketches of insertion tasks. By decoupling part description from placement and using sketch memory with point-cloud registration, the method achieves sub-millimetre precision in simulated environments. On 69 episodes, it successfully inserted 88% of unseen parts at the correct pose, significantly outperforming existing zero-shot agentic manipulation methods which failed on the same task.
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