Temporal Straightening for Latent Planning

2026-03-12

AI Summary

Although pretrained visual encoders offer strong semantic representations, their latent spaces are not optimized for planning tasks and often encode irrelevant or distracting information, leading to instability in gradient-based planning. Inspired by the human "perceptual straightening" hypothesis, this work introduces a temporal straightening mechanism into latent-space planning for the first time. By jointly training the encoder and dynamics predictor with curvature regularization, the method encourages locally straighter latent trajectories, thereby reducing the gap between Euclidean and geodesic distances and improving the condition number of the planning objective function. Experiments on multiple goal-reaching tasks demonstrate significant gains in both planning success rates and the stability of gradient-based optimization.

Technology Category

Humans and AI: Human-Aware Planning and Behavior PredictionPlanning, Routing, and Scheduling: Temporal PlanningIntelligent Robots: Motion and Path Planning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning. Using a curvature regularizer that encourages locally straightened latent trajectories, we jointly learn an encoder and a predictor. We show that reducing curvature this way makes the Euclidean distance in latent space a better proxy for the geodesic distance and improves the conditioning of the planning objective. We demonstrate empirically that temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks.

Problem

• latent planning
• representation learning
• world models
• visual encoders
• planning

Innovation

• temporal straightening
• latent planning
• curvature regularization
• world models
• representation learning