Imagine then Verify: Affordance-Targeted Active Perception for Task-Oriented Grasping in Cluttered Scenes

arXiv:2609.23504 · cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.RO

Submitted: 2026-09-20

Updated: 2026-09-20

Terminology

• Affordance — 150× in this paper · explained in 1 episode
• Verification — 25× in this paper · explained in 2 episodes
• Active Perception — 22× in this paper · explained in 2 episodes
• Grounding — 11× in this paper · explained in 3 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 3× in this paper
• Grasp Success Rate — 3× in this paper
• Information Gain — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• International Conference on Robotics and Automation — 1× in this paper
• Point Clouds — 1× in this paper · explained in 2 episodes

Sources

• OVAL-Grasp: Open-Vocabulary Affordance Localization for Task Oriented Grasping
• Scene Exploration by Vision-Language Models
• GPT-4o System Card
• TARGO: Benchmarking Target-driven Object Grasping under Occlusions

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving