Robotic Affordance Inference

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29 papers7 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

AffordJEV: Affordance Grounding by Confirming Candidate Regions
Nov 5, 2026Jincheng Li, Yutian Zhang, Jianyu Zhang +2Robotic Affordance Inference Visual Grounding for Robotics
Peking University
MolmoSpaces-CommonSense: Evaluating Commonsense in Robotic Manipulation Policies
Oct 30, 2026Arjun Guru, Max Argus, Jae Sung Park +3Robotic Manipulation Robot Manipulation Benchmarks
Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA · Allen Institute for AI (Ai2), Seattle, WA, USA · National University of Singapore, Singapore
PhysVLMM: Interaction-Conditioned Physical Belief Maps for Articulated Objects in Robot Scene Maps
Oct 29, 2026Dibyendu GhoshRobotic Manipulation Robotic Affordance Inference
Decoding Geometry, Not Tokens: Efficient VLMs for Robot Affordance and Trajectory Prediction
Oct 29, 2026Akila Ayanthi, Darshana Priyasad, Tharindu Fernando +4VLMs for Robotics Robot Motion Prediction
SAIVT, Queensland University of Technology, Australia · CSIRO Robotics, Australia · QUT Centre for Robotics, Australia
EgoContact: Evaluating Physical Intelligence with Egocentric Video
Oct 29, 2026Adam Deryło, Alexey Gavryushin, Daniel Cremers +2Robot Manipulation Datasets Robot Manipulation Benchmarks
University of Cambridge, United Kingdom · Technical University of Munich, Germany · ETH Zurich, Switzerland +3
UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception
Oct 21, 2026Linfei Li, Ruining Hu, Lin Zhang +5Robotic Affordance Inference VLMs for Robotics
School of Computer Science and Technology, Tongji University · School of Electrical Engineering and Computer Science, The University of Queensland · IEIT SYSTEMS (Beijing) Co., Ltd +1
Extending Affordance Segmentation for Wearable Robots to Multi-Object Images
Oct 21, 2026Simone Lugani, Matteo Montani, Paolo Gastaldo +2Robotic Affordance Inference Robotic Grasping
DITEN, University of Genoa, Genoa, Italy
Cross-Embodiment Affordance Transfer for Robotic Manipulation
Oct 13, 2026Sawera Yaseen, Serge Thill, Rekha RajaAffordance-Based Robotic Manipulation Robotic Affordance Inference
School of Artificial Intelligence, Donders Centre for Cognition, Department of Human-Centred Intelligent Systems, Radboud University, Nijmegen, The Netherlands.
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
Sep 16, 2026Jiadi You, Qize Yu, Yue Chen +11Affordance-Based Robotic Manipulation World Models for Robotics
The University of Hong Kong · The Hong Kong University of Science and Technology (Guangzhou) · Peking University +3
A4A: Cross-Embodiment Transfer of Action-Oriented 4D Affordances from Human Demonstrations
Sep 5, 2026Yifan Han, Litao Liu, Yuqi Gu +7Affordance-Based Robotic Manipulation Robotic Manipulation
Shanghai Jiao Tong University · Rutgers University–New Brunswick · The Hong Kong University of Science and Technology (GZ) +2
VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances
Aug 5, 2026Jihoon Oh, Kento Kawaharazuka, Kei OkadaRobot Learning Robot Skill Learning
University of Tokyo, Japan
AffordTrajDP: Dynamic Affordance-Guided Visuomotor Policy Learning for Robotic Manipulation
Aug 3, 2026Gaoyuan Wu, Ziyu Shan, Haoyang Du +2Visuomotor Policy Learning Robot Skill Learning
1Nanyang Technological University, Singapore
GROW $^2$ : Grounding Which and Where for Robot Tool Use
Jun 29, 2026Yuhong Deng, Yuyao Liu, David Hsu3D Visual Grounding Robotic Tool Use
National University of Singapore · Massachusetts Institute of Technology
RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation
Jun 25, 2026Guodong Zhang, Qichen He, Wenyuan Xie +6Motion Planning Constrained Motion Planning
School of Computer Science, Shanghai Jiaotong University, Shanghai, P. R. China
What Objects Enable, Not What They Are: Functional Latent Spaces for Affordance Reasoning
Jun 4, 2026Rohan Siva, Neel P. Bhatt, Yunhao Yang +6Latent Visual Reasoning Robot Task Planning
1The University of Texas at Austin · 2Neurosymbolic Intelligence · University of Colorado Boulder
AFUN: Towards an Affordance Foundation Model for Functionality Understanding
Jun 1, 2026Zhaoning Wang, Yi Zhong, Jiawei Fu +2Generalization in Robotic Manipulation Language-Conditioned Robot Manipulation
University of Michigan · University of California, San Diego · NVIDIA
StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
May 5, 2026Xiaowen Sun, Matthias Kerzel, Mengdi Li +3Physical Reasoning VLM Adaptation
Department of Informatics, University of Hamburg, Germany · King Abdullah University of Science and Technology, Saudi Arabia
Affordance Agent Harness: Verification-Gated Skill Orchestration
May 1, 2026Haojian Huang, Jiahao Shi, Yinchuan Li +1Multimodal Grounding LLM Agent Orchestration
HKUST(GZ) · Knowin AI · Harbin Engineering University +1
Assessing VLM-Driven Semantic-Affordance Inference for Non-Humanoid Robot Morphologies
Apr 21, 2026Jess Jones, Raul Santos-Rodriguez, Sabine HauertVLM Evaluation Cross-Embodiment Robot Learning
Bristol Robotics Laboratory, University of Bristol Bristol, United Kingdom · University of Bristol Bristol, United Kingdom
ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
Apr 16, 2026Pei-An Chen, Yong-Ching Liang, Jia-Fong Yeh +4Robot Task Planning Robot Manipulation Benchmarks
National Taiwan University · National Yang Ming Chiao Tung University · National Tsing Hua University
AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation
Apr 13, 2026Mingyang Li, Haofan Xu, Haowen Sun +10Affordance-Based Robotic Manipulation Robot Manipulation Datasets
School of Artificial Intelligence, Xi’an Jiaotong University
CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects
Apr 2, 2026Jingliang Li, Jindou Jia, Tuo An +73D Affordance Grounding Robotic Affordance Inference
MARS Lab, Nanyang Technological University, Singapore
AffordTissue: Dense Affordance Prediction for Tool-Action Specific Tissue Interaction
Apr 1, 2026Aiza Maksutova, Lalithkumar Seenivasan, Hao Ding +5Robotic Affordance Inference Surgical Robotics
Johns Hopkins University, Baltimore MD, USA
RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment
Mar 31, 2026Qiyuan Zhuang, He-Yang Xu, Yijun Wang +3Robotic Affordance Inference Affordance-Based Robotic Manipulation
School of Computer Science and Engineering, and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, Nanjing 211189, China · Southeast University-Monash University Joint Graduate School, Southeast University, Suzhou 215123, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgery
Mar 26, 2026Nan Xiao, Yunxin Fan, Farong Wang +1Robotic Affordance Inference Surgical Robotics
University of Tennessee, Knoxville, TN, USA · Stanford University, Palo Alto, CA, USA
Coupled Particle Filters for Robust Affordance Estimation
Mar 16, 2026Patrick Lowin, Vito Mengers, Oliver BrockRobotic Affordance Inference Robotic Manipulation
Robotics and Biology Laboratory, Technische Universität Berlin · Robotics Institute Germany · Science of Intelligence (SCIoI), Cluster of Excellence, Berlin, Germany
RoboPCA: Pose-centered Affordance Learning from Human Demonstrations for Robot Manipulation
Mar 8, 2026Zhanqi Xiao, Ruiping Wang, Xilin ChenRobotic Affordance Inference Affordance-Based Robotic Manipulation
The authors are with the Key Laboratory of AI Safety of CAS, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China, and also with the University of Chinese Academy of Sciences, Beijing 100049, China.
CRAFT-E: An Interpretable and Editable Neuro-Symbolic Framework for Affordance-Grounded Active Object Retrieval
Dec 3, 2025Zhou Chen, Joe Lin, Carson Bulgin +1Robotic Affordance Inference Robotic Object Search
CSSE Department, Auburn University, Auburn, 36849, AL, USA.
Actron3D: Learning Actionable Neural Functions from Videos for Transferable Robotic Manipulation
Oct 14, 2025Anran Zhang, Hanzhi Chen, Yannick Burkhardt +4Robot Learning from Video Affordance-Based Robotic Manipulation
ETH Zurich · Technical University of Munich

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AffordJEV: Affordance Grounding by Confirming Candidate Regions

Jincheng Li, Yutian Zhang, Jianyu Zhang, Xinshun Wang+1

Peking University

Robotic Affordance Inference Visual Grounding for Robotics

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Affordance grounding asks which visible part of an object performs a requested operation, a basic capability for embodied manipulation and human--robot interaction. Most existing methods follow a System Two paradigm: a multimodal language model first writes a chain of thought and then turns its conclusion into a box or points for a segmenter. This incurs considerable inference cost, gives the semantic decision little direct control over the extent of the final region, and makes mistakes hard to correct. Yet locating an affordance is essentially a perceptual judgment that need not be argued out in words. We therefore propose, in which frozen segmenters propose candidate regions and a System One model decides directly which of them perform the operation, generating no text and reading each decision from a single forward pass. Our contributions are twofold. (1) An inference framework that confirms candidate regions level by level, from object to part to sub-part, with evidence passed between the levels in both directions so that the levels correct one another. (2) A System One model trained for this framework: existing System One models are built for general-purpose decisions, judge one candidate per request and are ill-suited to the framework's region questions, so ours learns directly from the requests the framework itself produces and judges all candidates of a level at once. Trained only on InstructPart and evaluated with definition-aligned operation words, outperforms chain-of-thought methods in domain and in zero-shot transfer to UMD and IIT-AFF, is better overall than existing System One models run in the same framework, and needs fewer judge requests. To our knowledge, is the first approach to bring System One models to affordance grounding.