Summary of Manibox: Enhancing Spatial Grasping Generalization Via Scalable Simulation Data Generation, by Hengkai Tan et al.
ManiBox: Enhancing Spatial Grasping Generalization via Scalable Simulation Data Generation
by Hengkai Tan, Xuezhou Xu, Chengyang Ying, Xinyi Mao, Songming Liu, Xingxing Zhang, Hang Su, Jun Zhu
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of learning a precise robotic grasping policy, a crucial skill for embodied agents operating in complex real-world manipulation tasks. The authors identify that most models struggle with accurate spatial positioning of objects to be grasped, which they attribute to the extensive data requirements needed for adequate spatial understanding. To overcome this challenge, they present ManiBox, a novel bounding-box-guided manipulation method built on a simulation-based teacher-student framework. This approach efficiently generates scalable simulation data using bounding boxes, which uniquely determine the objects’ spatial positions. The student policy then utilizes these low-dimensional spatial states to enable zero-shot transfer to real robots. Through comprehensive evaluations in simulated and real-world environments, ManiBox demonstrates a marked improvement in spatial grasping generalization and adaptability to diverse objects and backgrounds. Additionally, the authors conduct an empirical study on scaling laws for policy performance, finding that spatial volume generalization scales with data volume in a power law. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots better grasp objects! Right now, most robotic grasping models struggle to accurately position objects. The problem is that they need a lot of data to understand how things are arranged in space. But collecting this data is hard and expensive. To fix this, the authors created ManiBox, a new way for robots to learn by using bounding boxes to help them understand where objects are. This works really well, even when the robot doesn’t have any experience with that object before! The paper also shows how good ManiBox is at generalizing what it’s learned to different situations. |
Keywords
» Artificial intelligence » Bounding box » Generalization » Scaling laws » Zero shot
