Review outlines three paths for embedding physical priors in robot learning
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A review categorizes approaches to embedding physical priors in robot learning into three paths: data and representation, model architecture, and training loss, and proposes that where a prior is located determines its constraining power. Using grasping a soft object as an example, the article explains that kinematics, dynamics, contact, and material properties act in layers within the same task; it also notes that physical priors are not a panacea and must match the task.
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- GuyuehomeReview: Three paths for embedding physical priors into robot learning, from classical mechanics to VLA and WAM
The article reviews three paths for embedding physical priors into robot learning: data and representation, model architecture, and training loss, and emphasizes that where a prior is placed determines its constraining effect. Using grasping a soft object as an example, it explains the layered roles of kinematics, dynamics, contact, and material properties in the task, while noting that physical priors are not a panacea and need to match the task.
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