AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.
Text-driven affordance learning from egocentric vision.Adv
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A DRL locomotion controller extended from prior quadruped work enabled the Go2-W robot to complete 2.8 km of autonomous real-world navigation including mixed terrain and stairs.
citing papers explorer
-
AFUN: Towards an Affordance Foundation Model for Functionality Understanding
AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.
-
Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning
A DRL locomotion controller extended from prior quadruped work enabled the Go2-W robot to complete 2.8 km of autonomous real-world navigation including mixed terrain and stairs.