RoboPCA: Pose-centered Affordance Learning from Human Demonstrations for Robot Manipulation
Understanding spatial affordances -- comprising the contact regions of object interaction and the corresponding contact poses -- is essential for robots to effectively manipulate objects and accomplish diverse tasks. However, existing spatial affordance prediction methods mainly focus on locating the contact regions while delegating the pose to independent pose estimation approaches, which can lead to task failures due to inconsistencies between predicted contact regions and candidate poses. In this work, we propose RoboPCA, a pose-centered affordance prediction framework that jointly predicts task-appropriate contact regions and poses conditioned on instructions. To enable scalable data ...
| Authors | Zhanqi Xiao, Ruiping Wang, Xilin Chen |
|---|---|
| Primary category | cs.RO |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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