Reproduce “Train What You Know -- Precise Pick-and-Place with Transporter Networks” on OpenResearch
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About this paper
Gergely Sóti, Xi Huang, Christian Wurll, Björn Hein
This research introduces several modifications to Transporter Networks, including an exact training method, iterative inference, and architectural refinements like feature cropping and query-rotated transport modules, to significantly enhance the precision of robotic pick-and-place operations. The proposed methods achieve up to 61% lower rotation errors and 68% lower translation errors in simulation, leading to a 95% success rate on a real robot task with only 10 demonstrations.
Key insights
- Explicitly providing exact ground-truth rotation examples during training, rather than discrete bins, significantly improves model precision and stability.
- Iteratively refining pose estimates at inference time can substantially boost accuracy without architectural changes.
- Computational efficiency can be maintained or improved in Transporter Networks by rotating learned feature embeddings instead of processing multiple rotated image patches, as demonstrated by the query-rotated transport module.
Summary by alphaXiv.
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