Latent Diffusion Planning: A New Approach to Imitation Learning That Leverages Suboptimal and Action-Free Data

Latent Diffusion Planning: A New Approach to Imitation Learning That Leverages Suboptimal and Action-Free Data

Imitation learning has made significant strides in recent years, thanks to policy architectures that scale to complex visuomotor tasks, multimodal distributions, and large datasets. However, these methods often rely on learning from vast amounts of expert demonstrations, which can be time-consuming and expensive to collect. To address these limitations, a team of researchers from Stanford and UC Berkeley has introduced Latent Diffusion Planning (LDP), a modular approach that leverages suboptimal and action-free data to improve imitation learning performance.

The Problem with Current Imitation Learning Methods

Traditional imitation learning methods, such as behavior cloning, require large datasets of expert demonstrations to map states to actions effectively. While recent advancements like Diffusion Policy and Action Chunking with Transformers have shown promise, they still struggle when expert data is limited. Additionally, these methods cannot easily incorporate suboptimal or action-free data—such as failed trajectories or human videos—which are often easier to collect but lack the precise action labels needed for supervised learning.

Introducing Latent Diffusion Planning (LDP)

LDP tackles these challenges by decoupling the imitation learning process into two components:

1. A Planner: This module forecasts future states in a learned latent space and can be trained on action-free data (e.g., videos of tasks without action labels).
2. An Inverse Dynamics Model (IDM): This module predicts actions between pairs of latent states and can be trained on suboptimal data (e.g., failed trajectories or imperfect demonstrations).

By separating planning from action prediction, LDP can leverage denser supervision signals from diverse data sources, making it more data-efficient than traditional methods.

How LDP Works

1. Learning a Compact Latent Space: LDP first trains a variational autoencoder (VAE) to compress high-dimensional image observations into a lower-dimensional latent space. This step ensures efficient planning and action prediction.
2. Training the Planner: Using a diffusion objective, the planner learns to forecast sequences of future latent states. Since this module doesn’t require action labels, it can be trained on action-free demonstrations.
3. Training the IDM: Another diffusion model, the IDM learns to predict actions between consecutive latent states. This module can be trained on suboptimal data, such as failed robot trajectories.

At inference time, LDP combines these components: the planner generates a sequence of future states, and the IDM extracts the corresponding actions. This closed-loop approach enables real-time, reactive control.

Key Advantages of LDP

• Data Efficiency: LDP outperforms state-of-the-art imitation learning methods when expert data is scarce by leveraging additional suboptimal and action-free data.
• Modularity: The separation of planning and action prediction allows each component to benefit from different types of data.
• Scalability: By planning in a latent space, LDP avoids the computational complexity of generating high-dimensional video frames, enabling faster inference.

Experimental Results

The researchers evaluated LDP on several simulated robotic manipulation tasks, including Robomimic Lift, Can, Square, and ALOHA Sim Transfer Cube. In low-demonstration regimes, LDP consistently outperformed baselines like Diffusion Policy and UniPi, particularly when augmented with suboptimal and action-free data. For example, on the Robomimic Lift task, LDP achieved a 100% success rate when trained with both action-free and suboptimal data, compared to 60% for standard Diffusion Policy.

In real-world experiments, LDP also demonstrated superior performance on a Franka Panda arm tasked with lifting a red block. The ability to reuse failed policy rollouts (suboptimal data) and action-free demonstrations proved critical in improving success rates.

Implications for Business and Robotics

LDP’s ability to learn from heterogeneous data sources has significant implications for scaling robotic learning in real-world applications. For businesses, this means:

• Reduced Data Collection Costs: Action-free data (e.g., human videos) is easier to acquire than expert demonstrations.
• Faster Deployment: By leveraging suboptimal data (e.g., failed trials), robots can learn more efficiently without requiring perfect demonstrations.
• Broader Applicability: LDP’s modular design makes it adaptable to various domains, from manufacturing to logistics.

Limitations and Future Work

While LDP shows promise, the researchers note that its latent space is learned via a simple VAE, which may not always capture the most useful features for control. Future work could explore more advanced representation learning techniques or integrate improvements in diffusion models to enhance performance further.

Conclusion

Latent Diffusion Planning represents a significant step forward in imitation learning by enabling robots to learn from diverse, imperfect data sources. By decoupling planning from action prediction and operating in a compact latent space, LDP offers a scalable and data-efficient solution for real-world robotic applications. As the field moves toward more generalist robot policies, methods like LDP will be crucial in bridging the gap between simulation and reality.

For more details, check out the full paper on arXiv and the project website.