Learning to Manipulate Anything: Revealing Data Scaling Laws in Bounding-Box Guided Policies

Description

Diffusion-based policies show limited generalization in semantic manipulation, posing a key obstacle to the deployment of real-world robots. This limitation arises because relying solely on text instructions is inadequate to direct the policy's attention toward the target object in complex and dynamic environments. To solve this problem, we propose leveraging bounding-box instruction to directly specify target object, and further investigate whether data scaling laws exist in semantic manipulati

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