5 papers · 1 filter
A4-Agent: An Agentic Framework for Zero-Shot Affordance Reasoning
Zixin Zhang, Kanghao Chen, Hanqing Wang +5
Affordance prediction, which identifies interaction regions on objects based on language instructions, is critical for embodied AI. Prevailing end-to-end models couple high-level r…
HOID-R1: Reinforcement Learning for Open-World Human-Object Interaction Detection Reasoning with Multimodal Large Language Model
Zhenhao Zhang, Hanqing Wang, Xiangyu Zeng +10
Understanding and recognizing human-object interaction (HOI) is a pivotal application in AR/VR and robotics. Recent open-vocabulary HOI detection approaches depend exclusively on l…
Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
Hanqing Wang, Shaoyang Wang, Yiming Zhong +7
Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It plays a vital role in the fields of hu…
SDEval: Safety Dynamic Evaluation for Multimodal Large Language Models
Hanqing Wang, Yuan Tian, Mingyu Liu +2
In the rapidly evolving landscape of Multimodal Large Language Models (MLLMs), the safety concerns of their outputs have earned significant attention. Although numerous datasets ha…
Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learning
Hanqing Wang, Zhenhao Zhang, Kaiyang Ji +12
3D affordance grounding aims to understand how diverse objects can be manipulated, making it a cornerstone of embodied interaction. However, prior works struggle to generalize to o…