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Affordance-Based Disambiguation of Surgical Instructions for Collaborative Robot-Assisted Surgery
Ana Davila, Jacinto Colan, Yasuhisa Hasegawa
https://arxiv.org/abs/2509.14967 h…

Affordance-Based Disambiguation of Surgical Instructions for Collaborative Robot-Assisted Surgery
Effective human-robot collaboration in surgery is affected by the inherent ambiguity of verbal communication. This paper presents a framework for a robotic surgical assistant that interprets and disambiguates verbal instructions from a surgeon by grounding them in the visual context of the operating field. The system employs a two-level affordance-based reasoning process that first analyzes the surgical scene using a multimodal vision-language model and then reasons about the instruction using …

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Hao Zhang, Zhen Kan, Weiwei Shang, Yongduan Song
https://arxiv.org/abs/2509.14939

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects
Despite recent advances in dexterous manipulations, the manipulation of articulated objects and generalization across different categories remain significant challenges. To address these issues, we introduce DART, a novel framework that enhances a diffusion-based policy with affordance learning and linear temporal logic (LTL) representations to improve the learning efficiency and generalizability of articulated dexterous manipulation. Specifically, DART leverages LTL to understand task semantic…

SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
Di Li, Jie Feng, Jiahao Chen, Weisheng Dong, Guanbin Li, Yuhui Zheng, Mingtao Feng, Guangming Shi
https://arxiv.org/abs/2507.23772

SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
3D affordance reasoning, the task of associating human instructions with the functional regions of 3D objects, is a critical capability for embodied agents. Current methods based on 3D Gaussian Splatting (3DGS) are fundamentally limited to single-object, single-step interactions, a paradigm that falls short of addressing the long-horizon, multi-object tasks required for complex real-world applications. To bridge this gap, we introduce the novel task of Sequential 3D Gaussian Affordance Reasonin…

Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
Hanqing Wang, Shaoyang Wang, Yiming Zhong, Zemin Yang, Jiamin Wang, Zhiqing Cui, Jiahao Yuan, Yifan Han, Mingyu Liu, Yuexin Ma
https://arxiv.org/abs/2508.06206

Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
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 human-robot interaction, human-object interaction, embodied manipulation, and embodied perception. Existing models often neglect the affordance shared among different objects because they lack the Chain-of-Thought(CoT) reasoning abilities, limiting their out-of-domain (OOD) generalization and explicit reasoning capabili…

Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework
Racquel Fygenson, Lace Padilla, Enrico Bertini
https://arxiv.org/abs/2509.09510 https:/…

Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework
Classically, affordance research investigates how the shape of objects communicates actions to potential users. Cognitive affordances, a subset of this research, characterize how the design of objects influences cognitive actions, such as information processing. Within visualization, cognitive affordances inform how graphs' design decisions communicate information to their readers. Although several related concepts exist in visualization, a formal translation of affordance theory to visualizati…

O$^3$Afford: One-Shot 3D Object-to-Object Affordance Grounding for Generalizable Robotic Manipulation
Tongxuan Tian, Xuhui Kang, Yen-Ling Kuo
https://arxiv.org/abs/2509.06233 ht…

O$^3$Afford: One-Shot 3D Object-to-Object Affordance Grounding for Generalizable Robotic Manipulation
Grounding object affordance is fundamental to robotic manipulation as it establishes the critical link between perception and action among interacting objects. However, prior works predominantly focus on predicting single-object affordance, overlooking the fact that most real-world interactions involve relationships between pairs of objects. In this work, we address the challenge of object-to-object affordance grounding under limited data contraints. Inspired by recent advances in few-shot lear…

PlanQA: A Benchmark for Spatial Reasoning in LLMs using Structured Representations
Fedor Rodionov, Abdelrahman Eldesokey, Michael Birsak, John Femiani, Bernard Ghanem, Peter Wonka
https://arxiv.org/abs/2507.07644

PlanQA: A Benchmark for Spatial Reasoning in LLMs using Structured Representations
We introduce PlanQA, a diagnostic benchmark for evaluating geometric and spatial reasoning in large-language models (LLMs). PlanQA is grounded in structured representations of indoor scenes, such as kitchens, living rooms, and bedrooms, encoded in a symbolic format (e.g., JSON, XML layouts). The benchmark includes diverse question types that test not only metric and topological reasoning (e.g., distance, visibility, shortest paths) but also interior design constraints such as affordance, cleara…

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
Haoyu Zhao, Linghao Zhuang, Xingyue Zhao, Cheng Zeng, Haoran Xu, Yuming Jiang, Jun Cen, Kexiang Wang, Jiayan Guo, Siteng Huang, Xin Li, Deli Zhao, Hua Zou
https://arxiv.org/abs/2508.08896

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors…

Affordance-Guided Dual-Armed Disassembly Teleoperation for Mating Parts
Gen Sako, Takuya Kiyokawa, Kensuke Harada, Tomoki Ishikura, Naoya Miyaji, Genichiro Matsuda
https://arxiv.org/abs/2508.05937

Affordance-Guided Dual-Armed Disassembly Teleoperation for Mating Parts
Robotic non-destructive disassembly of mating parts remains challenging due to the need for flexible manipulation and the limited visibility of internal structures. This study presents an affordance-guided teleoperation system that enables intuitive human demonstrations for dual-arm fix-and-disassemble tasks for mating parts. The system visualizes feasible grasp poses and disassembly directions in a virtual environment, both derived from the object's geometry, to address occlusions and structur…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Ruihai Wu, Kai Cheng, Yan Shen, Chuanruo Ning, Guanqi Zhan, Hao Dong

Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance
Neha Sunil, Megha Tippur, Arnau Saumell, Edward Adelson, Alberto Rodriguez
https://arxiv.org/abs/2509.03889

Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance
Manipulating clothing is challenging due to complex configurations, variable material dynamics, and frequent self-occlusion. Prior systems often flatten garments or assume visibility of key features. We present a dual-arm visuotactile framework that combines confidence-aware dense visual correspondence and tactile-supervised grasp affordance to operate directly on crumpled and suspended garments. The correspondence model is trained on a custom, high-fidelity simulated dataset using a distributi…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation
Yihe Tang, Wenlong Huang, Yingke Wang, Chengshu Li, Roy Yuan, Ruohan Zhang, Jiajun Wu, Li Fei-Fei

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/6]:
- AffordanceSAM: Segment Anything Once More in Affordance Grounding
Jiang, Wang, Li, Dang, Ma, Wei, Dai, Zhang, Wang

Hanging Around: Cognitive Inspired Reasoning for Reactive Robotics
Mihai Pomarlan, Stefano De Giorgis, Rachel Ringe, Maria M. Hedblom, Nikolaos Tsiogkas
https://arxiv.org/abs/2507.20832

Hanging Around: Cognitive Inspired Reasoning for Reactive Robotics
Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent's ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and im…

Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation
Kang Xiangli, Yage He, Xianwu Gong, Zehan Liu, Yuru Bai
https://arxiv.org/abs/2507.19242 htt…

Foundation Model-Driven Grasping of Unknown Objects via Center of Gravity Estimation
This study presents a grasping method for objects with uneven mass distribution by leveraging diffusion models to localize the center of gravity (CoG) on unknown objects. In robotic grasping, CoG deviation often leads to postural instability, where existing keypoint-based or affordance-driven methods exhibit limitations. We constructed a dataset of 790 images featuring unevenly distributed objects with keypoint annotations for CoG localization. A vision-driven framework based on foundation mode…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[3/3]:
- Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts
Huang, Yang, Zhu, Li, Shi, Zuo, Chen, Li, Yang