Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields
On the map: Diffusion models → Diffusion and flow policies for robotics
Abstract (arXiv)
Motivated by the problem of pursuit-evasion, we present a motion planning framework that combines energy-based diffusion models with artificial potential fields for robust real time trajectory generation in complex environments. Our approach processes obstacle information directly from point clouds, enabling efficient planning without requiring complete geometric representations. The framework employs classifier-free guidance training and integrates local potential fields during sampling to enhance obstacle avoidance. In dynamic scenarios, the system generates initial trajectories using the diffusion model and continuously refines them through potential field-based adaptation, demonstrating effective performance in pursuit-evasion scenarios with partial pursuer observability.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 20 of its 42 references that are in the map. First posted on arXiv 12 Jul 2025.
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- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2022Classifier-Free Diffusion Guidancecited by 5,847
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
- 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCcited by 220
- 2018Motion Planning Networkscited by 80
- 2017Continuous-Time Gaussian Process Motion Planning via Probabilistic Inferencecited by 49
- 2024Potential Based Diffusion Motion Planningcited by 41
7 more
- 2017Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Searchcited by 23
- 2023Sampling-Based Motion Planning: A Comparative Reviewcited by 21
- 2021Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systemscited by 15
- 2020Learning Obstacle Representations for Neural Motion Planningcited by 13
- 2022Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision riskcited by 5
- 2024Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learningcited by 2
- 2018Motion planning in high-dimensional spacescited by 1
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- 2023Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
- 2024PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation
- 2025Diffusion-based Planning with Learned Viability Filters
- 2026Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling
- 2025Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing
- 2026Zero-Shot Reactive Obstacle Avoidance for Generative Robot Policies
- 2025A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model