Enhancing Diffusion Planners with Automatic Feasibility Detection for Reliable Behavior Synthesis

Diffusion-based planning has emerged as a powerful technique for tackling long-horizon, sparse-reward tasks. By training trajectory diffusion models and conditioning the sampled trajectories using auxiliary guidance functions, these planners can generate complex behaviors. However, one significant drawback is that diffusion models are not guaranteed to produce feasible plans, leading to failed executions and making them less suitable for safety-critical applications.

In a recent paper titled "Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans," Kyowoon Lee, Seongun Kim, and Jaesik Choi introduce a novel approach to refine unreliable plans generated by diffusion models. This method leverages a new metric called the restoration gap (RG) to identify and correct infeasible plans.

Key Technical Contributions

• Restoration Gap Metric: The authors propose the restoration gap as a measure of how far a given plan is from being feasible. This metric is estimated using a gap predictor, which provides guidance for refining the diffusion planner.
• Gap Predictor: A neural network that takes an infeasible plan as input and outputs a restoration gap score. This score indicates the level of refinement needed to make the plan viable.
• Attribution Map Regularizer: To prevent adversarial refining guidance from sub-optimal gap predictors, the authors introduce an attribution map regularizer. This technique helps identify error-prone transitions in the plan, allowing for more precise and effective refinements.

Implementation Details

• Model Architecture:

  • The diffusion model is trained to generate trajectories over a long horizon.
  • The gap predictor is a separate neural network that takes the generated trajectory as input and outputs the restoration gap score.
  • The attribution map regularizer is applied during training to ensure that the gap predictor does not produce adversarial guidance.

• Training Process:

  • The diffusion model is first trained using a combination of reinforcement learning (RL) and auxiliary tasks to condition the trajectories.
  • The gap predictor is then trained on a dataset of infeasible plans, with the goal of accurately estimating the restoration gap.
  • During refinement, the gap predictor's output is used to guide the diffusion planner towards more feasible solutions.

Benchmarks and Results

The authors evaluate their approach on three different benchmarks in offline control settings that require long-horizon planning:

• Mujoco Environments: The method shows significant improvements in plan feasibility compared to baseline diffusion planners.
• Robotic Manipulation Tasks: The restoration gap metric effectively identifies and corrects errors in the generated plans, leading to more reliable execution.
• Navigation Tasks: The approach demonstrates its ability to handle complex environments with sparse rewards, further validating its effectiveness.

Explainability

One of the key strengths of this approach is its explainability. By using attribution maps, the authors can highlight error-prone transitions in the plan, providing insights into why certain plans are infeasible. This not only helps in refining the plans but also aids in understanding the behavior synthesis process.

Conclusion

The work by Lee, Kim, and Choi addresses a critical issue in diffusion-based planning: the generation of infeasible plans. By introducing the restoration gap metric and leveraging an attribution map regularizer, they provide a robust framework for refining unreliable plans. This approach not only improves the reliability of behavior synthesis but also offers valuable insights into the plan refinement process.