  Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in
human environments is an important yet challenging task for future
home-assistant robots. The space of 3D articulated objects is exceptionally
rich in their myriad semantic categories, diverse shape geometry, and
complicated part functionality. Previous works mostly abstract kinematic
structure with estimated joint parameters and part poses as the visual
representations for manipulating 3D articulated objects. In this paper, we
propose object-centric actionable visual priors as a novel
perception-interaction handshaking point that the perception system outputs
more actionable guidance than kinematic structure estimation, by predicting
dense geometry-aware, interaction-aware, and task-aware visual action
affordance and trajectory proposals. We design an interaction-for-perception
framework VAT-Mart to learn such actionable visual representations by
simultaneously training a curiosity-driven reinforcement learning policy
exploring diverse interaction trajectories and a perception module summarizing
and generalizing the explored knowledge for pointwise predictions among diverse
shapes. Experiments prove the effectiveness of the proposed approach using the
large-scale PartNet-Mobility dataset in SAPIEN environment and show promising
generalization capabilities to novel test shapes, unseen object categories, and
real-world data. Project page: https://hyperplane-lab.github.io/vat-mart
