Our new #ICML2025 paper, led by @ZhaoHaibo47588, presents a hierarchical equivariance architecture that enables multi-level sample efficiency in visuomotor policy learning. Check it out for more details!
Our new #ICML2025 paper, led by @ZhaoHaibo47588, presents a hierarchical equivariance architecture that enables multi-level sample efficiency in visuomotor policy learning. Check it out for more details!
Join us on Monday October. 14th at 2pm (UTC+4) in #IROS2024 Workshop on Equivariant Robotics.
A great lineup of keynote speakers will discuss how symmetry penetrates each and every subfield of robotics.
Website and Zoom Link: equirob2024.github.io
Instead of inferring a desired object pose directly, this method "imagines" a reconstruction of the entire scene in the target pose. Surprisingly, we find that this improves sample efficiency, even though we are inferring more information.
Instead of inferring a desired object pose directly, this method "imagines" a reconstruction of the entire scene in the target pose. Surprisingly, we find that this improves sample efficiency, even though we are inferring more information.
Please check our latest work: Equivariant Diffusion Policy! This is fantastic work, which significantly improves the generalization ability, accuracy, and efficiency of diffusion policy!
Please check our latest work: Equivariant Diffusion Policy! This is fantastic work, which significantly improves the generalization ability, accuracy, and efficiency of diffusion policy!
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Robotic manipulation, learning, perception, planning, control, and other stuff.
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