D4orm: 動力学を考慮した拡散ノイズ除去変形によるマルチロボット軌道生成
D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations
拡散モデルのノイズ除去を利用し、ロボットの動力学モデルと適合度関数のみから、衝突のない実行可能なマルチロボット軌道を最適化する手法を提案。最大16台のロボットでMPPIより高速に高品質解を発見し、8機のドローンで実機展開を示した。
著者: Yuhao Zhang, Keisuke Okumura, Heedo Woo, Ajay Shankar, Amanda Prorok
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
