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UniRAP is a unified model that infers part-level physical affordances and translates language instructions into actionable geometric representations. It treats the task as a conditional multimodal generation problem, integrating visual inputs, textual instructions, and optional prompts into a shared spatiotemporal representation via a unified interface token mechanism. The model employs a Unified Affordance Decoder to jointly perform object detection, part-level affordance segmentation, and 4‑DoF interaction pose estimation, and uses a curriculum-based transfer training strategy to improve data efficiency with limited textual annotations.
arXiv:2609.37264v1 Announce Type: cross Abstract: Affordance perception aims to localize actionable regions supporting embodied interaction, yet 2D and 3D affordance grounding have evolved as separat...
arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.
arXiv:2609.22332v1 Announce Type: cross Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...
arXiv:2605.03927v3 Announce Type: replace Abstract: Vision-language models have demonstrated strong performance across robotic perception and instruction-following tasks. However, they still struggle...
AffordSim is a scalable data generator and benchmark that integrates open‑vocabulary 3D affordance prediction into simulation‑based trajectory generation for robotic manipulation. Given a natural‑language task description, it synthesizes task‑relevant scenes, queries affordances, grounds them on object surfaces, samples region‑conditioned grasps, and selects executable candidates with motion planning, while randomizing pose, texture, lighting, noise, and backgrounds for sim‑to‑real transfer. The benchmark includes 50 tasks, five robot embodiments, and over 500 objects, achieving 93% of the success rate of manual contact annotations on affordance‑critical tasks and 89% on hard composite tasks, with policies trained on AffordSim transferring zero‑shot to a real Franka FR3 with 24% average success.