  Learning generalizable visual dynamic representation across different
embodied environments is crucial for real-world robotic manipulation. As the
scale and diversity of robot demonstration data are limited, recent works have
turned to large-scale pre-training using human data. However, the morphological
differences between humans and robots introduce a significant human-robot
domain discrepancy, challenging the generalization of these human-data
pre-trained models to downstream manipulation tasks. To address this, we
propose a novel adaptation paradigm that utilizes readily available paired
human-robot video data to bridge the discrepancy. Following this paradigm, our
method exploits a human-robot contrastive alignment loss to align the semantics
of human and robot videos, adapting pre-trained models to the robotic domain in
a parameter-efficient manner. The experiments demonstrate significant
improvements on 25 tasks across three different benchmarks, where the
single-task, language-conditioned multi-task settings are covered, and two
different pre-trained models are evaluated. On the large RLBench benchmark, our
adaptation method achieves an average improvement of $8.9\%$ in success rate
over the pre-trained R3M model across multiple tasks. We will release the code
and models upon acceptance.
