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Perceiving and manipulating 3D articulated objects in diverse environments isessential for home-assistant robots. Recent studies have shown that point-levelaffordance provides actionable priors for downstream manipulation tasks.However, existing works primarily focus on single-object scenarios withhomogeneous agents, overlooking the realistic constraints imposed by theenvironment and the agent's morphology, e.g., occlusions and physicallimitations. In this paper, we propose an environment-aware affordanceframework that incorporates both object-level actionable priors and environmentconstraints. Unlike object-centric affordance approaches, learningenvironment-aware affordance faces the challenge of combinatorial explosion dueto 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 oftraining on scenes containing a single occluder and generalizing to scenes withcomplex occluder combinations. Experiments demonstrate the effectiveness of ourproposed approach in learning affordance considering environment constraints.Project page at https://chengkaiacademycity.github.io/EnvAwareAfford/

Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions