Lifting Embodied World Models for Planning and Control

2022-06-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Researchers have developed a "Lifted World Model" (LWM) to enhance planning and control for embodied agents, particularly those with high-dimensional action spaces like human-like robots. This method addresses the computational expense of search-based planning, such as the Cross-Entropy Method (CEM), which scales poorly with action dimensionality. The LWM framework integrates a lightweight policy that translates low-dimensional, high-level actions into sequences of high-dimensional, low-level joint actions, which then feed into a frozen world model. For a human-like embodiment, the high-level actions are defined as 2D waypoints projected onto the current observation frame, targeting leaf joints like the pelvis, head, and hands. This approach significantly improves planning efficiency and effectiveness, achieving a 3.8x lower mean joint error to the goal pose compared to direct low-level joint space searching, while also generalizing to environments not seen during policy training.

Key takeaway

For research scientists developing embodied AI agents, you should consider implementing a lifted world model approach, particularly when dealing with high-dimensional action spaces. This method, using visually interpretable 2D waypoints, offers a substantial improvement in planning efficiency and accuracy (3.8x lower mean joint error) over direct low-level action space search. Your team can achieve better performance on long-horizon tasks and enhance generalization to novel environments without modifying the base world model, making complex control more tractable.

Key insights

Lifting world models with low-dimensional waypoints significantly improves embodied agent planning efficiency and accuracy.

Principles

• High-level actions simplify complex control.
• Waypoints are effective visual goal signals.
• Policies can generalize to unseen environments.

Method

A lightweight policy maps 2D waypoints (high-level actions) to sequences of low-level joint actions, which then drive a frozen world model to predict future observations, enabling efficient search-based planning.

In practice

• Use 2D waypoints for intuitive goal specification.
• Employ waypoint masking for sparse input handling.
• Integrate DINOv3-S encoder for visual context.

Topics

• Lifted World Model
• Waypoint Planning
• Embodied AI
• Cross-Entropy Method
• Human-like Embodiment

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.