Our goal is to enable robots to plan sequences of tabletop actions to push a block with unknown physical properties to a desired goal pose on the table. We approach this problem by learning the constituent models of a Partially-Observable Markov Decision Process (POMDP), where the robot can observe the outcome of a push, but the physical properties of the block that govern the dynamics remain unknown. The pushing problem is a difficult POMDP to solve due to the challenge of state estimation. The physical properties have a nonlinear relationship with the outcomes, requiring computationally expensive methods, such as particle filters, to represent beliefs. Leveraging the Attentive Neural Process architecture, we propose to replace the particle filter with a neural network that learns the inference computation over the physical properties given a history of actions. This Neural Process is integrated into planning as the Neural Process Tree with Double Progressive Widening (NPT-DPW). Simulation results indicate that NPT-DPW generates more effective plans faster than traditional particle filter methods, even in complex pushing scenarios.
View on arXiv@article{jain2025_2504.17924,
title={ Learning Attentive Neural Processes for Planning with Pushing Actions },
author={ Atharv Jain and Seiji Shaw and Nicholas Roy },
journal={arXiv preprint arXiv:2504.17924},
year={ 2025 }
}