mlsanityData to 7 Oct 2026

PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation

arXiv 2409.16012 ↗First posted 24 Sep 2024cs.ROcited by 11

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

We introduce a learning-guided motion planning framework that generates seed trajectories using a diffusion model for trajectory optimization. Given a workspace, our method approximates the configuration space (C-space) obstacles through an environment representation consisting of a sparse set of task-related key configurations, which is then used as a conditioning input to the diffusion model. The diffusion model integrates regularization terms that encourage smooth, collision-free trajectories during training, and trajectory optimization refines the generated seed trajectories to correct any colliding segments. Our experimental results demonstrate that high-quality trajectory priors, learned through our C-space-grounded diffusion model, enable the efficient generation of collision-free trajectories in narrow-passage environments, outperforming previous learning- and planning-based baselines. Videos and additional materials can be found on the project page: https://kiwi-sherbet.github.io/PRESTO.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 28 of its 46 references that are in the map. First posted on arXiv 24 Sep 2024.

Builds on

28 of 46 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023EDMP: Ensemble-of-costs-guided Diffusion for Motion Planning20 Sep
  2. 2023Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models3 Aug
  3. 2023Diffusion-based Generation, Optimization, and Planning in 3D Scenes15 Jan
  4. 2022Is Conditional Generative Modeling all you need for Decision-Making?28 Nov

Other references · 24

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  6. 2022Scalable Diffusion Models with Transformerscited by 5,931
  7. 2022Classifier-Free Diffusion Guidancecited by 5,717
  8. 2020Point Transformercited by 1,462
  9. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  10. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 768
  11. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  12. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
12 more
  1. 2017Learning Sampling Distributions for Robot Motion Planningcited by 79
  2. 2019Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Plannerscited by 62
  3. 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generationcited by 51
  4. 2023cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generationcited by 46
  5. 2020Object Rearrangement Using Learned Implicit Collision Functionscited by 43
  6. 2019LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planningcited by 19
  7. 2019Learning-Based Proxy Collision Detection for Robot Motion Planning Applicationscited by 17
  8. 2023CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generationcited by 17
  9. 2018Learning Implicit Sampling Distributions for Motion Planningcited by 16
  10. 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networkscited by 16
  11. 2018Deep sequential models for sampling-based planningcited by 7
  12. 2021DiffCo: Auto-Differentiable Proxy Collision Detection with Multi-class Labels for Safety-Aware Trajectory Optimizationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 6.

11 papers.

  1. 2025Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Treescited by 3
  2. 2025ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusioncited by 3
  3. 2025Diffusion-Guided Multi-Arm Motion Planningcited by 2
  4. 2025CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidancecited by 1
  5. 2026Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunitiescited by 1
  6. 2025FlightDiffusion: Revolutionising Autonomous Drone Training with Diffusion Models Generating FPV Videocited by 0
  7. 2026Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusioncited by 0
  8. 2026Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulatorscited by 0
  9. 2026Muninn: Your Trajectory Diffusion Model But Fastercited by 0
  10. 2026BridgeFlow: Fast and Robust SE(2)-Equivariant Motion Planning with Flow Matchingcited by 0
  11. 2026Zero-Shot Reactive Obstacle Avoidance for Generative Robot Policiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models
  2. 2026Amortizing Trajectory Diffusion with Keyed Drift Fields
  3. 2025Tree-Guided Diffusion Planner
  4. 2025Cascaded Diffusion Models for Neural Motion Planning
  5. 2025A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model
  6. 2024LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
  7. 2024Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
  8. 2024Constraint-Aware Diffusion Models for Trajectory Optimization