robotics
A Digital Twin Turned a Semantic Grasp Into a Physical One
GraspTwin treated foundation-model suggestions as seeds, then tested nearby poses in randomized physics.
Summary
GraspTwin treated foundation-model suggestions as seeds, then tested nearby poses in randomized physics.
GraspTwin begins with one RGB-D observation, builds a digital twin and asks a foundation model for task-relevant grasp proposals such as using a mug handle for pouring. Rather than executing those proposals directly, it searches nearby poses with Bayesian optimization and evaluates batches in domain-randomized physics. The authors report zero-shot real-world transfer in minutes and task-oriented grasp success up to 33% above compared pipelines. The figure is specific to their objects, robot, simulator and baselines.
Why it matters
GraspTwin treated foundation-model suggestions as seeds, then tested nearby poses in randomized physics.
Limits and context
No additional limitation was separately recorded.
Key claims
GraspTwin treated foundation-model suggestions as seeds, then tested nearby poses in randomized physics.
Evidence: source-2026-09-28-010
Sources
- arXiv preprint 2609.30543arXiv · primary research
Corrections
No corrections have been recorded for this story.