4.6 Article

Toward Affordance Detection and Ranking on Novel Objects for Real-World Robotic Manipulation

Journal

IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 4, Issue 4, Pages 4070-4077

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2930364

Keywords

Perception for grasping and manipulation; deep learning in robotics and automation; RGB-D perception

Categories

Funding

  1. National Science Foundation [1605228]
  2. Div Of Chem, Bioeng, Env, & Transp Sys
  3. Directorate For Engineering [1605228] Funding Source: National Science Foundation

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This letter presents a framework to detect and rank affordances of novel objects to assist with robotic manipulation tasks. The framework segments the affordance map of unseen objects using region-based affordance segmentation. Detected affordances define an initial state from which to generate action primitives for manipulation via the planning domain definition language (PDDL). The proposed category-agnostic affordance segmentation approach generalizes learned affordances to unseen objects by utilizing binary classification on proposed instance masks. The predicted pixel-wise level affordances are ranked by KL-divergence, augmenting the available affordance choices for manipulation tasks with non-primary affordances of an object. Experimental results show that the proposed method achieves state-of-the-art performance on affordance segmentation of novel objects, and outperforms baselines on affordance ranking. Actual robotic manipulation scenarios demonstrate the use of affordance detection with PDDL-generated action primitives for task execution. Prediction of ranked affordances on unseen objects provides flexibility to accomplish goal-oriented tasks.

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