Conventional behavioral-cloning policies commonly pool an image encoder's features into one global vector, then use a multilayer perceptron to regress robot coordinates.
Camera geometry makes robot policies more data-efficient and position-robust
BIND links candidate 3D end-effector positions to projected image features, avoiding the need to learn this spatial correspondence from demonstrations.
Big Tech
Cameron Smith · Arsh Tangri · Vitor Guizilini · Yue Wang · Zubair Irshad · Sergey Zakharov
University of Southern California · Toyota Research Institute
Research Digest··2 min read
Smith et al.
Why this paper
From Toyota Research Institute and University of Southern California
In one line
BIND improves robot policy data efficiency and robustness by binding 3D actions to 2D image features via camera projection.
What we could check
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