Embodied World Models Emerge from Navigational Task in Open-Ended Environments
TLDR
A recurrent agent trained with sparse rewards in mazes spontaneously develops compact embodied world models for spatial reasoning.
Reasoning
The paper presents a novel dynamical systems and representation analysis to show that embodied world models emerge from sensorimotor interaction, with strong causal evidence. However, it is limited to simulated planar mazes and lacks real-world validation, and the connection to generative or video world models is indirect.
Read-first score
Read-first score 55, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 34.
Field roles
Rank sensitivity
Stability: volatile; rank range: 312.
Keyword Scores
Deep Analysis
Innovations
- Demonstrating that a recurrent agent trained solely with sparse rewards on procedurally generated planar mazes can internalize metric concepts such as direction, distance, and obstacle layout without explicit supervision.
- Casting the closed agent-environment loop as a hybrid dynamical system and identifying stable limit cycles in the agent's state space as evidence of an internal world model.
- Introducing a Ridge Representation that embeds whole trajectories into a common metric space for analyzing neural and behavioral manifolds.
- Using canonical correlation analysis to expose a robust linear alignment between neural and behavioral manifolds.
- Performing targeted perturbations of the most informative neural dimensions to causally link internal representations to navigation performance.
Methodology
The authors train a recurrent agent with sparse rewards on procedurally generated planar mazes. After training, they analyze the agent's internal representations by modeling the closed agent-environment loop as a hybrid dynamical system, identifying stable limit cycles, and using a Ridge Representation to embed trajectories into a common metric space. They then apply canonical correlation analysis to measure alignment between neural and behavioral manifolds and conduct perturbation experiments on informative neural dimensions.
Key Results
The trained agent consistently produces near-optimal paths in unseen mazes. Canonical correlation analysis reveals a robust linear alignment between neural and behavioral manifolds, and perturbations of the most informative neural dimensions sharply degrade navigation performance.
Limitations
- The study is limited to planar mazes and a specific navigational task, so generalizability to more complex or realistic environments is not established.
- The emergence of world models may depend on the recurrent architecture and sparse reward training setup, and the analysis methods (hybrid dynamical system, Ridge Representation) may be specific to this setting.
- The claimed 'embodied world models' are restricted to metric concepts (direction, distance, obstacle layout) and may not extend to richer semantic or causal understanding.
- The paper does not address transfer of learned representations to other tasks or environments beyond the maze navigation paradigm.