Hierarchical Planning with Latent World Models

1. 1
   Identify MPC Limitations

   Recognize that traditional Model Predictive Control (MPC) with learned world models struggles with long-horizon tasks due to error accumulation. Assess your current embodied AI projects for scenarios where this limitation is critical.
2. 2
   Design a Hierarchical Planning Structure

   Architect a hierarchical planning framework. This involves breaking down long-horizon tasks into a sequence of shorter, manageable sub-goals, where higher-level plans guide lower-level actions to mitigate error propagation.
3. 3
   Integrate Latent World Models

   Incorporate latent world models within your hierarchical planning. These models should learn compressed, abstract representations of the environment, enabling more robust predictions over longer time horizons for both high-level planning and low-level control.
4. 4
   Develop Robustness Mechanisms

   Implement mechanisms to handle uncertainties and adapt to dynamic environments. This may include re-planning triggers, uncertainty-aware prediction, or adaptive control strategies based on feedback from the latent world model, ensuring stable operation despite prediction errors.
5. 5
   Evaluate Long-Horizon Performance

   Rigorously test your hierarchical system on complex, multi-step tasks that traditionally challenge MPC. Measure metrics like task completion rate, efficiency, and error tolerance to validate the improved robustness and extended operational capabilities.

import torch
import torch.nn as nn

class LatentWorldModel(nn.Module):
    def __init__(self, obs_dim, action_dim, latent_dim):
        super().__init__()
        # Example: Encoder to latent state
        self.encoder = nn.Sequential(
            nn.Linear(obs_dim, 128),
            nn.ReLU(),
            nn.Linear(128, latent_dim)
        )
        # Example: Latent dynamics model
        self.dynamics = nn.Sequential(
            nn.Linear(latent_dim + action_dim, 128),
            nn.ReLU(),
            nn.Linear(128, latent_dim)
        )
        # Example: Decoder from latent state
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.ReLU(),
            nn.Linear(128, obs_dim)
        )

    def forward(self, observation, action):
        # Encode observation to latent state
        latent_state = self.encoder(observation)
        # Predict next latent state
        next_latent_state = self.dynamics(torch.cat([latent_state, action], dim=-1))
        # Decode latent state to predicted observation
        predicted_observation = self.decoder(next_latent_state)
        return predicted_observation, next_latent_state

class HierarchicalPlanner:
    def __init__(self, low_level_controller, high_level_planner, latent_world_model):
        self.low_level_controller = low_level_controller # e.g., MPC
        self.high_level_planner = high_level_planner     # e.g., Task planner
        self.latent_world_model = latent_world_model

    def plan_and_act(self, current_observation, goal):
        # High-level plan (e.g., sequence of sub-goals)
        high_level_plan = self.high_level_planner.generate_plan(current_observation, goal, self.latent_world_model)

        actions = []
        for sub_goal in high_level_plan:
            # Low-level control for each sub-goal using latent world model for prediction
            low_level_actions = self.low_level_controller.control(current_observation, sub_goal, self.latent_world_model)
            actions.extend(low_level_actions)
            # (Simulate execution and update current_observation for next sub_goal)
            # current_observation = self.simulate_execution(low_level_actions, current_observation)
        return actions

# This is a conceptual starter. Full implementation requires significant research and development.
# It demonstrates the structural components: a LatentWorldModel and a HierarchicalPlanner orchestrating
# high-level planning and low-level control based on the world model's predictions.

• Paperarxiv.org