Robotics: Science and Systems XXI

DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories

Jean-Baptiste Bouvier, Kanghyun Ryu, Qiayuan Liao, Koushil Sreenath, Negar Mehr

Abstract:

Diffusion models excel at creating images and videos thanks to their multimodal generative capabilities. These same capabilities have made diffusion models increasingly popular in robotics research, where they are extensively used for generating robot motion. However, the stochastic nature of diffusion models is fundamentally at odds with the precise dynamical equations describing the feasible motion of robots. Hence, generating dynamically admissible robot trajectories is a challenge for diffusion models. To alleviate this issue, we introduce DDAT: Diffusion policies for Dynamically Admissible Trajectories to generate admissible trajectories of black-box robotic systems using diffusion models. To generate such trajectories our diffusion policies project their predictions onto a dynamically admissible manifold during both training and inference to align the objective of the denoiser neural network with the dynamical admissibility constraint. Due to the auto-regressive nature of such projections as well as the black-box nature of robot dynamics, trajectory projections are challenging. We thus enforce admissibility by iteratively sampling a polytopic under-approximation of the reachable set of a state onto which we project its predicted successor, before iterating this process with the projected successor. By producing accurate trajectories, this projection eliminates the need for diffusion models to continually replan, enabling one-shot long-horizon trajectory planning. We demonstrate that our proposed framework generates higher quality dynamically admissible robot trajectories through extensive simulations on a quadcopter and various MuJoCo environments, along with real-world experiments on a Unitree GO1 and GO2. Code available on our website: https://iconlab.negarmehr.com/DDAT/.

Download:

Bibtex:

  
@INPROCEEDINGS{BouvierJ-RSS-25, 
    AUTHOR    = {Jean-Baptiste Bouvier AND Kanghyun Ryu AND Qiayuan Liao AND Koushil Sreenath AND Negar Mehr}, 
    TITLE     = {{DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2025}, 
    ADDRESS   = {LosAngeles, CA, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2025.XXI.078} 
}