Behavioral Score Diffusion: Model-Free Trajectory Planning via Kernel-Based Score Estimation from Data

• URL: http://arxiv.org/abs/2604.00391v1
• Date: Wed, 01 Apr 2026 02:21:53 GMT
• Title: Behavioral Score Diffusion: Model-Free Trajectory Planning via Kernel-Based Score Estimation from Data
• Abstract summary: We introduce emphBehavioral Score Diffusion (BSD), a training-free and model-free trajectory planner.<n>BSD computes the diffusion score function directly from a library of trajectory data via kernel-weighted estimation.<n>BSD substantially outperforms nearest-neighbor retrieval (18--63% improvement)
• Score: 11.599621915522802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based trajectory optimization has emerged as a powerful planning paradigm, but existing methods require either learned score networks trained on large datasets or analytical dynamics models for score computation. We introduce \emph{Behavioral Score Diffusion} (BSD), a training-free and model-free trajectory planner that computes the diffusion score function directly from a library of trajectory data via kernel-weighted estimation. At each denoising step, BSD retrieves relevant trajectories using a triple-kernel weighting scheme -- diffusion proximity, state context, and goal relevance -- and computes a Nadaraya-Watson estimate of the denoised trajectory. The diffusion noise schedule naturally controls kernel bandwidths, creating a multi-scale nonparametric regression: broad averaging of global behavioral patterns at high noise, fine-grained local interpolation at low noise. This coarse-to-fine structure handles nonlinear dynamics without linearization or parametric assumptions. Safety is preserved by applying shielded rollout on kernel-estimated state trajectories, identical to existing model-based approaches. We evaluate BSD on four robotic systems of increasing complexity (3D--6D state spaces) in a parking scenario. BSD with fixed bandwidth achieves 98.5\% of the model-based baseline's average reward across systems while requiring no dynamics model, using only 1{,}000 pre-collected trajectories. BSD substantially outperforms nearest-neighbor retrieval (18--63\% improvement), confirming that the diffusion denoising mechanism is essential for effective data-driven planning.

Related papers

• Bayesian Flow Networks for Offline Trajectory Planning [0.0]
  offline reinforcement learning (RL) leverages static datasets to learn decision policies without real-time environment interaction.<n>We present BFN-RL, a unified generative modeling framework for offline RL based on Bayesian Flow Networks.
  arXiv  Detail & Related papers  (2026-08-25T21:19:35Z)
• Amortizing Trajectory Diffusion with Keyed Drift Fields [2.5352713493505785]
  Keyed Drifting Policies is a one-step trajectory generator trained with a drift-field objective.<n>It repels generated trajectories toward condition-matched dataset windows.<n>It achieves strong performance with one-step inference and substantially lower planning latency than diffusion sampling.
  arXiv  Detail & Related papers  (2026-03-14T17:56:15Z)
• Domain-Incremental Continual Learning for Robust and Efficient Keyword Spotting in Resource Constrained Systems [0.0]
  Keywords Spotting systems with small footprint models deployed on edge devices face significant accuracy and robustness challenges.<n>We propose a comprehensive framework for continual learning designed to adapt to new domains while maintaining computational efficiency.<n>The proposed pipeline integrates a dual-input Convolutional Neural Network, utilizing both Mel Frequency Cepstral Coefficients (MFCC) and Mel-spectrogram features.
  arXiv  Detail & Related papers  (2026-01-22T17:59:31Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery [12.33811209316863]
  We propose Score-informed Neural Operator (SciNO) to approximate the Hessian diagonal of the log-densities.<n>SciNO reduces order divergence by 42.7% on synthetic graphs and by 31.5% on real-world datasets.<n>We also propose a probabilistic control algorithm for causal reasoning with autoregressive models.
  arXiv  Detail & Related papers  (2025-08-18T06:25:41Z)
• Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling [70.8832906871441]
  We study how to steer generation toward desired rewards without retraining the models.<n>Prior methods typically resample or filter within a single denoising trajectory, optimizing rewards step-by-step without trajectory-level refinement.<n>We introduce particle Gibbs sampling for diffusion language models (PG-DLM), a novel inference-time algorithm enabling trajectory-level refinement while preserving generation perplexity.
  arXiv  Detail & Related papers  (2025-07-11T08:00:47Z)
• Continuous Visual Autoregressive Generation via Score Maximization [69.67438563485887]
  We introduce a Continuous VAR framework that enables direct visual autoregressive generation without vector quantization.<n>Within this framework, all we need is to select a strictly proper score and set it as the training objective to optimize.
  arXiv  Detail & Related papers  (2025-05-12T17:58:14Z)
• Predicting Forced Responses of Probability Distributions via the Fluctuation-Dissipation Theorem and Generative Modeling [0.0]
  We present a data-driven framework for estimating the response of higher-order moments of nonlinear systems to small external perturbations.<n>We combine GFDT with score-based generative modeling to estimate the system's score function directly from data.<n>Our method is validated on several models relevant to climate dynamics.
  arXiv  Detail & Related papers  (2025-04-17T20:54:33Z)
• A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading [0.0]
  In engineering, models are central to capturing soil behavior across diverse drainage conditions, stress paths,and loading histories.<n>This study introduces a recursive Bayesian neural network (rBNN) framework that unifies temporal sequence learning with generalized inference.<n>The framework is validated against four datasets spanning both simulated and experimental triaxial tests.
  arXiv  Detail & Related papers  (2025-01-17T10:15:03Z)
• Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence [19.484676783876306]
  Diffusion models have emerged as powerful tools for generative modeling.<n>We propose a control framework for fine-tuning diffusion models.<n>We show that PI-FT achieves global convergence at a linear rate.
  arXiv  Detail & Related papers  (2024-12-24T04:55:46Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Score-based Diffusion Models in Function Space [137.70916238028306]
  Diffusion models have recently emerged as a powerful framework for generative modeling.<n>This work introduces a mathematically rigorous framework called Denoising Diffusion Operators (DDOs) for training diffusion models in function space.<n>We show that the corresponding discretized algorithm generates accurate samples at a fixed cost independent of the data resolution.
  arXiv  Detail & Related papers  (2023-02-14T23:50:53Z)
• Scaling Structured Inference with Randomization [64.18063627155128]
  We propose a family of dynamic programming (RDP) randomized for scaling structured models to tens of thousands of latent states. Our method is widely applicable to classical DP-based inference. It is also compatible with automatic differentiation so can be integrated with neural networks seamlessly.
  arXiv  Detail & Related papers  (2021-12-07T11:26:41Z)

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.