robotics
The Gripper Learned Its Own Surface Before Seeing the Object
GOAG introduces object features only at inference time and samples contacts from a learned representation of the hand.
Summary
GOAG introduces object features only at inference time and samples contacts from a learned representation of the hand.
The generative planner starts from the geometric fact that gripper and object surfaces coincide at valid contacts, then models the contact distribution for a specific gripper without object-specific training data. The authors report an 86.93 percent average success rate on MultiDex objects plus simulated and real-world tests across multiple grippers; the claim remains tied to the reported protocols, not universal dexterity.
Why it matters
GOAG introduces object features only at inference time and samples contacts from a learned representation of the hand.
Limits and context
- The authors report an 86.93 percent average success rate on MultiDex objects plus simulated and real-world tests across multiple grippers; the claim remains tied to the reported protocols, not universal dexterity.
Key claims
GOAG introduces object features only at inference time and samples contacts from a learned representation of the hand.
Qualification: The authors report an 86.93 percent average success rate on MultiDex objects plus simulated and real-world tests across multiple grippers; the claim remains tied to the reported protocols, not universal dexterity.
Evidence: source-2026-08-23-008
Sources
- arXiv preprint 2608.19759arXiv · primary research
Corrections
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