Multi-step planning with learned effects of partial action executions

• Main Authors: Aktas, Hakan, Bozdogan, Utku, Ugur, Emre
• Format: Journal Article
• Language: English
• Published: 16-03-2023
• Subjects: Computer Science - Robotics
• DOI: 10.48550/arxiv.2303.09355

In this paper, we propose a novel affordance model, which combines object,
action, and effect information in the latent space of a predictive neural
network architecture that is built on Conditional Neural Processes. Our model
allows us to make predictions of intermediate effects expected to be obtained
during action executions and make multi-step plans that include partial
actions. We first compared the prediction capability of our model using an
existing interaction data set and showed that it outperforms a recurrent neural
network-based model in predicting the effects of lever-up actions. Next, we
showed that our model can generate accurate effect predictions for other
actions, such as push and grasp actions. Our system was shown to generate
successful multi-step plans to bring objects to desired positions using the
traditional A* search algorithm. Furthermore, we realized a continuous planning
method and showed that the proposed system generated more accurate and
effective plans with sequences of partial action executions compared to plans
that only consider full action executions using both planning algorithms.