  Perceiving and interacting with 3D articulated objects, such as cabinets,
doors, and faucets, pose particular challenges for future home-assistant robots
performing daily tasks in human environments. Besides parsing the articulated
parts and joint parameters, researchers recently advocate learning manipulation
affordance over the input shape geometry which is more task-aware and
geometrically fine-grained. However, taking only passive observations as
inputs, these methods ignore many hidden but important kinematic constraints
(e.g., joint location and limits) and dynamic factors (e.g., joint friction and
restitution), therefore losing significant accuracy for test cases with such
uncertainties. In this paper, we propose a novel framework, named AdaAfford,
that learns to perform very few test-time interactions for quickly adapting the
affordance priors to more accurate instance-specific posteriors. We conduct
large-scale experiments using the PartNet-Mobility dataset and prove that our
system performs better than baselines.
