Temporal Logic Guidance for Action-Only Diffusion Policies with World Models

• URL: http://arxiv.org/abs/2606.22729v1
• Date: Mon, 22 Jun 2026 00:12:44 GMT
• Title: Temporal Logic Guidance for Action-Only Diffusion Policies with World Models
• Abstract summary: Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time.<n>We propose a novel guidance method for action-only diffusion policies that uses a separate learned world model to enable differentiable evaluation of STL.<n>This steers behavior toward constraint satisfaction without retraining, improving constraint adherence while preserving task performance.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control is desirable in human-robot settings. Prior solutions to this lack of control have utilized Signal Temporal Logic (STL) to express human intentions and provide corresponding guidance for diffusion policy inference. However, these approaches can only guide diffusion policies that jointly generate future actions and states, increasing both complexity and runtime. We propose a novel guidance method for action-only diffusion policies that uses a separate learned world model to enable differentiable evaluation of STL robustness, with its gradient then injected into the diffusion process. This steers behavior toward constraint satisfaction without retraining, improving constraint adherence while preserving task performance. On the Can Transport task from Robomimic, our method maintains 100% task success while reducing constraint violations from over 80% for baseline methods to 4%. We also discuss extensions toward improved robustness and more complex constraints.

Related papers

• V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think [90.69263509098948]
  We introduce Variational GRPO, a method that integrates ELBO-based surrogates with the Group Relative Policy Optimization algorithm.<n>V-GRPO achieves state-of-the-art performance in text-to-image synthesis, while delivering a $2times$ speedup over MixGRPO and a $3times$ speedup over DiffusionNFT.
  arXiv  Detail & Related papers  (2026-04-25T17:03:21Z)
• Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach [78.4812458793128]
  We propose textbfTACO, a test-time-scaling framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks.<n>Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits.
  arXiv  Detail & Related papers  (2025-12-02T14:42:54Z)
• Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking [29.920087317401396]
  Generative Behavior Cloning is a simple yet effective framework for robot learning.<n>We propose two novel techniques to enhance the consistency and reactivity of diffusion policies.<n>Our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-14T11:16:34Z)
• HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model [54.64088247291416]
  A fundamental objective of manipulation policy design is to endow robots to comprehend human instructions, reason about scene cues, and execute generalized actions in dynamic environments.<n>Recent autoregressive vision-language-action (VLA) methods inherit common-sense reasoning capabilities from vision-language models (VLMs) for next action-token prediction.<n>We introduce HybridVLA, a unified framework that absorbs the continuous nature of diffusion-based actions and the contextual reasoning of autoregression.
  arXiv  Detail & Related papers  (2025-03-13T17:59:52Z)
• Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models [4.0342998431529695]
  We propose a method that allows diffusion policies to actively seek human assistance only when necessary, reducing reliance on constant human oversight.<n>We leverage the generative process of diffusion policies to compute an uncertainty-based metric based on which the autonomous agent can decide to request operator assistance at deployment time.<n>We show that the same method can be used for efficient data collection for fine-tuning diffusion policies in order to improve their autonomous performance.
  arXiv  Detail & Related papers  (2025-02-26T15:12:29Z)
• Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learning [13.332006760984122]
  Action-Constrained Reinforcement Learning (ACRL) employs a projection layer after the policy network to correct the action.<n>Recent methods were proposed to train generative models to learn a differentiable mapping between latent variables and feasible actions.
  arXiv  Detail & Related papers  (2025-02-08T12:58:26Z)
• Diffusion Predictive Control with Constraints [51.91057765703533]
  Diffusion predictive control with constraints (DPCC) is an algorithm for diffusion-based control with explicit state and action constraints.<n>We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints.
  arXiv  Detail & Related papers  (2024-12-12T15:10:22Z)
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning [70.20191211010847]
  Offline reinforcement learning (RL) aims to learn an optimal policy using a previously collected static dataset. We introduce Diffusion Q-learning (Diffusion-QL) that utilizes a conditional diffusion model to represent the policy. We show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks.
  arXiv  Detail & Related papers  (2022-08-12T09:54:11Z)
• Model-based Safe Reinforcement Learning using Generalized Control Barrier Function [6.556257209888797]
  This paper proposes a model-based feasibility enhancement technique of constrained RL. By using the model information, the policy can be optimized safely without violating actual safety constraints. The proposed method achieves up to four times fewer constraint violations and converges 3.36 times faster than baseline constrained RL approaches.
  arXiv  Detail & Related papers  (2021-03-02T08:17:38Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.