Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans

• URL: http://arxiv.org/abs/2310.19427v1
• Date: Mon, 30 Oct 2023 10:35:42 GMT
• Title: Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
• Authors: Kyowoon Lee, Seongun Kim and Jaesik Choi
• Abstract summary: Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks. However, due to their nature as generative models, diffusion models are not guaranteed to generate feasible plans. We propose a novel approach to refine unreliable plans generated by diffusion models by providing refining guidance to error-prone plans.
• Score: 25.326624139426514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks by training trajectory diffusion models and conditioning the sampled trajectories using auxiliary guidance functions. However, due to their nature as generative models, diffusion models are not guaranteed to generate feasible plans, resulting in failed execution and precluding planners from being useful in safety-critical applications. In this work, we propose a novel approach to refine unreliable plans generated by diffusion models by providing refining guidance to error-prone plans. To this end, we suggest a new metric named restoration gap for evaluating the quality of individual plans generated by the diffusion model. A restoration gap is estimated by a gap predictor which produces restoration gap guidance to refine a diffusion planner. We additionally present an attribution map regularizer to prevent adversarial refining guidance that could be generated from the sub-optimal gap predictor, which enables further refinement of infeasible plans. We demonstrate the effectiveness of our approach on three different benchmarks in offline control settings that require long-horizon planning. We also illustrate that our approach presents explainability by presenting the attribution maps of the gap predictor and highlighting error-prone transitions, allowing for a deeper understanding of the generated plans.

Related papers

• Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion [40.76697924496143]
  We propose an adaptive generative planning approach that adjusts replanning frequency based on the uncertainty of action predictions. Our method minimizes the need for frequent, computationally expensive, and redundant replanning while maintaining robust collision avoidance performance.
  arXiv  Detail & Related papers  (2024-09-25T14:03:58Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree [20.855596726996712]
  Trajectory Aggregation Tree (TAT) is a dynamic tree-like structure based on historical and current trajectories. TAT can be deployed without modifying the original training and sampling pipelines of diffusion planners, making it a training-free, ready-to-deploy solution. Our results highlight its remarkable ability to resist the risk from unreliable trajectories, guarantee the performance boosting of diffusion planners in $100%$ of tasks, and exhibit an appreciable tolerance margin for sample quality, thereby enabling planning with a more than $3times$ acceleration.
  arXiv  Detail & Related papers  (2024-05-28T06:57:22Z)
• Simple Hierarchical Planning with Diffusion [54.48129192534653]
  Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. We introduce the Hierarchical diffuser, a fast, yet surprisingly effective planning method combining the advantages of hierarchical and diffusion-based planning. Our model adopts a "jumpy" planning strategy at the higher level, which allows it to have a larger receptive field but at a lower computational cost.
  arXiv  Detail & Related papers  (2024-01-05T05:28:40Z)
• Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty [56.30846158280031]
  Task planning for embodied AI has been one of the most challenging problems. We propose a task-agnostic method named 'planning as in-painting' The proposed framework achieves promising performances in various embodied AI tasks.
  arXiv  Detail & Related papers  (2023-12-02T10:07:17Z)
• SafeDiffuser: Safe Planning with Diffusion Probabilistic Models [97.80042457099718]
  Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees. We propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation.
  arXiv  Detail & Related papers  (2023-05-31T19:38:12Z)
• Planning with Diffusion for Flexible Behavior Synthesis [125.24438991142573]
  We consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories.
  arXiv  Detail & Related papers  (2022-05-20T07:02:03Z)
• Counterfactual Plans under Distributional Ambiguity [12.139222986297263]
  We study the counterfactual plans under model uncertainty, in which the distribution of the model parameters is partially prescribed. First, we propose an uncertainty quantification tool to compute the lower and upper bounds of the probability of validity for any given counterfactual plan. We then provide corrective methods to adjust the counterfactual plan to improve the validity measure.
  arXiv  Detail & Related papers  (2022-01-29T03:41:47Z)
• Forethought and Hindsight in Credit Assignment [62.05690959741223]
  We work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated.
  arXiv  Detail & Related papers  (2020-10-26T16:00:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.