🎯Can robots do better on planning involving neural dynamics?
Introducing our recent work, BaB-ND: https://t.co/gQnPOypyOm.
Existing sampling- and gradient-based methods may fail short on complex planning problems over non-convex neural dynamics with long planning horizons.
Inspired by neural network verification techniques, robots can now strategically explore and reduce the search space and tackle challenging tasks like non-prehensile planar pushing with obstacles.
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This project is led by @Keyi_Shen_ and made possible through the incredible contributions of co-authors: Jiangwei Yu, Jose Barreiros, @huan_zhang12, and @YunzhuLiYZ.
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🎯Can robots do better on planning involving neural dynamics?
Introducing our recent work, BaB-ND: https://t.co/gQnPOypyOm.
Existing sampling- and gradient-based methods may fail short on complex planning problems over non-convex neural dynamics with long planning horizons.
Inspired by neural network verification techniques, robots can now strategically explore and reduce the search space and tackle challenging tasks like non-prehensile planar pushing with obstacles.
(1/6)
BaB-ND is an effective, applicable, and scalable framework, excelling in a wide range of real-world complex planning problems.
These include contact-rich manipulation, deformable object handling, and object pile management, all while supporting diverse model architectures such as multilayer perceptrons (MLPs) and graph neural networks (GNNs).
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BaB-ND draws inspiration from the successful neural network verifier α,β-CROWN (🔗 https://t.co/pmqArEyJi1).
We present a novel adaptation of neural network verification techniques to address planning problems involving neural dynamics models in robotic manipulation.
Our work bridges the efforts in both the robotics and verification communities.
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