  Perceiving and manipulating 3D articulated objects in diverse environments is
essential for home-assistant robots. Recent studies have shown that point-level
affordance provides actionable priors for downstream manipulation tasks.
However, existing works primarily focus on single-object scenarios with
homogeneous agents, overlooking the realistic constraints imposed by the
environment and the agent's morphology, e.g., occlusions and physical
limitations. In this paper, we propose an environment-aware affordance
framework that incorporates both object-level actionable priors and environment
constraints. Unlike object-centric affordance approaches, learning
environment-aware affordance faces the challenge of combinatorial explosion due
to the complexity of various occlusions, characterized by their quantities,
geometries, positions and poses. To address this and enhance data efficiency,
we introduce a novel contrastive affordance learning framework capable of
training on scenes containing a single occluder and generalizing to scenes with
complex occluder combinations. Experiments demonstrate the effectiveness of our
proposed approach in learning affordance considering environment constraints.
Project page at https://chengkaiacademycity.github.io/EnvAwareAfford/
