# Multi-step planning with learned effects of partial action executions

arXiv: https://arxiv.org/abs/2303.09355 · HTML: https://mlsanity.com/paper/2303.09355/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2023.

## Built on by (most cited first, 4 of 4)

- [Interleaved Multitask Learning with Energy Modulated Learning Progress](https://mlsanity.com/paper/2504.00707/) (2025)
- [Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning](https://mlsanity.com/paper/2410.10045/) (2024)
- [Cross-Embodied Affordance Transfer through Learning Affordance Equivalences](https://mlsanity.com/paper/2404.15648/) (2024)
- [Affordance-Based Manipulation Planning with Text Goals and Sim-to-Real Generalisation via Real-to-Sim Image Conversion](https://mlsanity.com/paper/2607.11004/) (2026)

## Abstract (arXiv)

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.