学习遮挡下3D铰接物体操作的环境感知可供性
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
AI总结：
提出环境感知可供性框架，结合物体先验与环境约束，通过对比学习解决遮挡复杂性导致的组合爆炸，实现从单一遮挡到复杂遮挡的泛化。
摘要：
在多样环境中感知和操作3D铰接物体对家庭辅助机器人至关重要。近期研究表明，点级可供性为下游操作任务提供了可执行的先验。然而，现有工作主要关注具有同质智能体的单物体场景，忽略了环境和智能体形态施加的现实约束，如遮挡和物理限制。本文提出一个环境感知可供性框架，结合物体级可执行先验和环境约束。与以物体为中心的可供性方法不同，学习环境感知可供性面临组合爆炸挑战，这源于各种遮挡的复杂性，表现为数量、几何形状、位置和姿态。为解决此问题并提升数据效率，我们引入一种新颖的对比可供性学习框架，能够在包含单一遮挡物的场景上训练，并泛化到具有复杂遮挡物组合的场景。实验证明了所提方法在考虑环境约束下学习可供性的有效性。项目页面：https://chengkaiacademycity.github.io/EnvAwareAfford/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
