D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915373005144064 |
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| author | Zhang, Yuhao Okumura, Keisuke Woo, Heedo Shankar, Ajay Prorok, Amanda |
| author_facet | Zhang, Yuhao Okumura, Keisuke Woo, Heedo Shankar, Ajay Prorok, Amanda |
| contents | This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations Zhang, Yuhao Okumura, Keisuke Woo, Heedo Shankar, Ajay Prorok, Amanda Robotics Systems and Control This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. |
| title | D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2503.12204 |