TD-MPC: How World Models Enable Robot Control

Fri, 07 Aug 2026 00:00:00 +0000

In the previous article, we broke down RSSM’s dual-track state design and understood how the Dreamer series “imagines” the future in latent space. But RSSM is not the only approach to using world models for robot control. Today we discuss another important route: TD-MPC (Temporal Difference Model Predictive Control).

If Dreamer’s core idea is to leverage a learned world model to perform imagined rollouts in latent space and optimize the policy via actor-critic methods, then TD-MPC takes a more direct approach: learn a world model, then plan within that model to select the optimal action sequence for execution. Rather than relying on a policy network as the sole decision-making mechanism, it uses the world model for online planning, combined with a learned policy prior to improve search efficiency.

Robot Control on WorldSense Tech Blog

TD-MPC: How World Models Enable Robot Control