Sum of Costs Diffusion with Dynamic Guidance for Motion Planning
- URL: http://arxiv.org/abs/2605.24690v1
- Date: Sat, 23 May 2026 17:59:15 GMT
- Title: Sum of Costs Diffusion with Dynamic Guidance for Motion Planning
- Abstract summary: We present a method that generates collision-free trajectories using diffusion models where the denoising process is guided by the gradient of the total collision cost.<n> Experimental results demonstrate that guiding the diffusion model dynamically with the sum of collision costs offers more robust performance.
- Score: 0.6187780920448871
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The motion planning problem for robotic manipulation can be addressed through classical or deep learning approaches. Existing methods face significant challenges in generalizing to diverse settings. In this study, we present a method with high generalization capability that generates collision-free trajectories using diffusion models where the denoising process is guided by the gradient of the total collision cost. We are also presenting a dynamic approach for choosing start step of the gradient guidance. Experimental results demonstrate that guiding the diffusion model dynamically with the sum of collision costs offers more robust performance by overcoming the generalization issues faced by competing methods. The proposed model demonstrates its effectiveness by achieving the highest performance on diverse test settings in M$π$nets\ dataset among the compared methods.
Related papers
- Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning [5.5818653283217055]
Multi-Robot Motion Planning in continuous environments is challenging due to the growth of the joint trajectory space and the difficulty of enforcing dynamic feasibility and hard safety constraints.<n>Recent approaches recast trajectory planning as probabilistic inference, sampling from a posterior over trajectories using diffusion models whose score functions are learned from demonstration data.<n>We introduce Model-Based Diffusion Optimal Control (MDOC), a model-based diffusion planner that efficiently produces dynamically feasible trajectories without relying on data.
arXiv Detail & Related papers (2026-07-14T06:54:03Z) - One step further with Monte-Carlo sampler to guide diffusion better [11.58337125468802]
We propose an additional backward denois- ing step and Monte-Carlo sampling (ABMS) to achieve better guided diffu- sion.<n>We conduct experiments across various task settings and data types, mainly includ- ing conditional online handwritten trajectory generation.<n> Experimental results demonstrate that our approach can be effec- tively used with higher order samplers.
arXiv Detail & Related papers (2026-03-04T05:48:02Z) - Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics [49.242224984144904]
We propose Euphonium, a novel framework that steers generation via process reward gradient guided dynamics.<n>Our key insight is to formulate the sampling process as a theoretically principled algorithm that explicitly incorporates the gradient of a Process Reward Model.<n>We derive a distillation objective that internalizes the guidance signal into the flow network, eliminating inference-time dependency on the reward model.
arXiv Detail & Related papers (2026-02-04T08:59:57Z) - Bounding Distributional Shifts in World Modeling through Novelty Detection [15.354352209595973]
We use a variational autoencoder as a novelty detector to ensure that proposed action trajectories during planning do not cause the learned model to deviate from the training data distribution.<n>The proposed method improves over state-of-the-art solutions in terms of data efficiency.
arXiv Detail & Related papers (2025-08-08T07:42:14Z) - DISC: Dynamic Decomposition Improves LLM Inference Scaling [83.5708537758088]
Inference scaling methods for LLMs often rely on decomposing problems into steps, followed by sampling and selecting the best next steps.<n>We propose dynamic decomposition, a method that adaptively and automatically partitions solution and reasoning traces into manageable steps during inference.
arXiv Detail & Related papers (2025-02-23T20:37:32Z) - Aligning Few-Step Diffusion Models with Dense Reward Difference Learning [81.85515625591884]
Stepwise Diffusion Policy Optimization (SDPO) is an alignment method tailored for few-step diffusion models.
SDPO incorporates dense reward feedback at every intermediate step to ensure consistent alignment across all denoising steps.
SDPO consistently outperforms prior methods in reward-based alignment across diverse step configurations.
arXiv Detail & Related papers (2024-11-18T16:57:41Z) - Training-free Diffusion Model Alignment with Sampling Demons [15.400553977713914]
We propose an optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining.<n>Our approach works by controlling noise distribution in denoising steps to concentrate density on regions corresponding to high rewards through optimization.<n>Our experiments show that the proposed approach significantly improves the average aesthetics scores text-to-image generation.
arXiv Detail & Related papers (2024-10-08T07:33:49Z) - Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints [55.39203337683045]
We propose to perform optimization within the data manifold using diffusion models.<n>Depending on the differentiability of the objective function, we propose two different sampling methods.<n>Our method achieves better or comparable performance with previous state-of-the-art baselines.
arXiv Detail & Related papers (2024-02-28T03:09:12Z) - Unsupervised Discovery of Interpretable Directions in h-space of
Pre-trained Diffusion Models [63.1637853118899]
We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models.
We employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself.
By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions.
arXiv Detail & Related papers (2023-10-15T18:44:30Z) - DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions [52.63323657077447]
We propose DNMOT, an end-to-end trainable DeNoising Transformer for multiple object tracking.
Specifically, we augment the trajectory with noises during training and make our model learn the denoising process in an encoder-decoder architecture.
We conduct extensive experiments on the MOT17, MOT20, and DanceTrack datasets, and the experimental results show that our method outperforms previous state-of-the-art methods by a clear margin.
arXiv Detail & Related papers (2023-09-09T04:40:01Z) - Distributionally Robust Model-based Reinforcement Learning with Large
State Spaces [55.14361269378122]
Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment.
We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets.
We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
arXiv Detail & Related papers (2023-09-05T13:42:11Z) - Sample Efficient Reinforcement Learning via Model-Ensemble Exploration
and Exploitation [3.728946517493471]
MEEE is a model-ensemble method that consists of optimistic exploration and weighted exploitation.
Our approach outperforms other model-free and model-based state-of-the-art methods, especially in sample complexity.
arXiv Detail & Related papers (2021-07-05T07:18:20Z) - Sample-efficient reinforcement learning using deep Gaussian processes [18.044018772331636]
Reinforcement learning provides a framework for learning to control which actions to take towards completing a task through trial-and-error.
In model-based reinforcement learning efficiency is improved by learning to simulate the world dynamics.
We introduce deep Gaussian processes where the depth of the compositions introduces model complexity while incorporating prior knowledge on the dynamics brings smoothness and structure.
arXiv Detail & Related papers (2020-11-02T13:37:57Z)
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.