ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning

2025-10-23

AI Summary

This work addresses the challenge of disentangling controllable variables in unsupervised robotic pose estimation, where confounding factors—including joint angles, geometric structure, illumination, background, and camera configuration—obscure causal relationships. We propose ROPES, the first intervention-based causal representation learning framework tailored for real-world robotic systems. Grounded in score-based modeling, ROPES leverages distribution shifts induced by actuator-driven interventions to identify and isolate intervenable latent variables—such as joint angles and end-effector pose—without any labeled data. Its key contribution is the first successful deployment of intervention-based causal representation learning on physical robots, enabling high-fidelity disentanglement of generative factors. In semi-synthetic robotic arm experiments, ROPES significantly outperforms existing semi-supervised baselines, achieving pose reconstruction accuracy closely aligned with ground-truth values.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Causal representation learning (CRL) has emerged as a powerful unsupervised framework that (i) disentangles the latent generative factors underlying high-dimensional data, and (ii) learns the cause-and-effect interactions among the disentangled variables. Despite extensive recent advances in identifiability and some practical progress, a substantial gap remains between theory and real-world practice. This paper takes a step toward closing that gap by bringing CRL to robotics, a domain that has motivated CRL. Specifically, this paper addresses the well-defined robot pose estimation -- the recovery of position and orientation from raw images -- by introducing Robotic Pose Estimation via Score-Based CRL (ROPES). Being an unsupervised framework, ROPES embodies the essence of interventional CRL by identifying those generative factors that are actuated: images are generated by intrinsic and extrinsic latent factors (e.g., joint angles, arm/limb geometry, lighting, background, and camera configuration) and the objective is to disentangle and recover the controllable latent variables, i.e., those that can be directly manipulated (intervened upon) through actuation. Interventional CRL theory shows that variables that undergo variations via interventions can be identified. In robotics, such interventions arise naturally by commanding actuators of various joints and recording images under varied controls. Empirical evaluations in semi-synthetic manipulator experiments demonstrate that ROPES successfully disentangles latent generative factors with high fidelity with respect to the ground truth. Crucially, this is achieved by leveraging only distributional changes, without using any labeled data. The paper also includes a comparison with a baseline based on a recently proposed semi-supervised framework. This paper concludes by positioning robot pose estimation as a near-practical testbed for CRL.

Problem

• Unsupervised robotic pose estimation from raw images without labeled data
• Disentangling controllable latent variables via causal representation learning
• Identifying generative factors through interventions on robotic actuators

Innovation

• Unsupervised causal representation learning for robotics
• Score-based disentanglement of controllable latent variables
• Interventional learning using actuator commands without labels