mlsanityData to 7 Oct 2026

RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset

arXiv 2412.19500 ↗First posted 27 Dec 2024cs.RO cs.LGcited by 0

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

Abstract (arXiv)

Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DoFs and complex, dynamic environments. While traditional motion planning algorithms struggle with high-dimensional spaces, deep learning-based methods often face instability and inefficiency in complex tasks. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators. By integrating physical constraints with a point cloud encoder and replacing the U-Net structure with an encoder-only transformer, RobotDiffuse improves the model's ability to capture temporal dependencies and generate smoother, more coherent motion plans. We validate the approach using a complex simulator and release a new dataset, Robot-obtalcles-panda (ROP), with 35M robot poses and 0.14M obstacle avoidance scenarios. The highest overall score obtained in the experiment demonstrates the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The dataset can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 14 of its 19 references that are in the map. First posted on arXiv 27 Dec 2024.

Builds on

14 of 19 references.

Closest ancestors · 2

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

Other references · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  7. 2022Human Motion Diffusion Modelcited by 1,062
  8. 2021Robust Motion In-betweeningcited by 223
  9. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 68
  10. 2019Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Plannerscited by 62
  11. 2022Motion Policy Networkscited by 53
  12. 2017Continuous-Time Gaussian Process Motion Planning via Probabilistic Inferencecited by 49

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design
  2. 2026Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulators
  3. 2024Multi-Robot Motion Planning with Diffusion Models
  4. 2025AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation
  5. 2025Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Models
  6. 2025Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
  7. 2023Speeding Up Optimization-based Motion Planning through Deep Learning
  8. 2024Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation