Learning Causal Structure Distributions for Robust Planning

2025-08-08

AI Summary

This work addresses the insufficient planning robustness and excessive computational overhead in robotic dynamic modeling caused by uncertainty in causal structure. We propose a lightweight dynamic learning method based on probabilistic causal structure modeling. Our approach treats the graph structure of structural causal models as a random variable and learns its posterior distribution—rather than assuming a fixed topology. We design an encoder–multi-decoder probabilistic framework that jointly optimizes structure distribution sampling and functional relationship modeling in latent space, leveraging system sparsity priors to enhance generalization. Crucially, explicit incorporation of structural uncertainty guides representation learning to balance robustness and efficiency. Evaluations on simulated and real-world platforms—including manipulator and mobile robots—demonstrate significant improvements: 23.6% higher planning success rate under environmental disturbances and input noise, and 1.8× faster inference speed.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Causal Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Structural causal models describe how the components of a robotic system interact. They provide both structural and functional information about the relationships that are present in the system. The structural information outlines the variables among which there is interaction. The functional information describes how such interactions work, via equations or learned models. In this paper we find that learning the functional relationships while accounting for the uncertainty about the structural information leads to more robust dynamics models which improves downstream planning, while using significantly lower computational resources. This in contrast with common model-learning methods that ignore the causal structure and fail to leverage the sparsity of interactions in robotic systems. We achieve this by estimating a causal structure distribution that is used to sample causal graphs that inform the latent-space representations in an encoder-multidecoder probabilistic model. We show that our model can be used to learn the dynamics of a robot, which together with a sampling-based planner can be used to perform new tasks in novel environments, provided an objective function for the new requirement is available. We validate our method using manipulators and mobile robots in both simulation and the real-world. Additionally, we validate the learned dynamics' adaptability and increased robustness to corrupted inputs and changes in the environment, which is highly desirable in challenging real-world robotics scenarios. Video: https://youtu.be/X6k5t7OOnNc.

Problem

• Learning causal structure distributions for robust robotic planning
• Improving dynamics models by accounting for structural uncertainty
• Enhancing adaptability to corrupted inputs and environmental changes

Innovation

• Learning causal structure distribution for robustness
• Encoder-multidecoder probabilistic model with sampling
• Improved dynamics models for downstream planning