拡散モデルと一貫性蒸留による人間協調ロボット軌道生成の高速化
Accelerating Human-Aware Robot Trajectory Generation via Diffusion and Consistency Distillation
人間とロボットの協調環境で、衝突回避を考慮した関節空間軌道を拡散モデルで生成し、一貫性蒸留で推論を高速化する手法を提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Byeong-Il Ham, Hyun-Bin Kim, Kyung-Soo Kim
分類: cs.RO
原文アブストラクト
This research proposes a constrained motion planning framework for robot manipulators in human-robot interaction (HRI). For a non-redundant manipulator with a fully specified end-effector pose, additional requirements such as collision avoidance and self-collision avoidance are difficult to handle as simple null-space secondary tasks. This limitation makes it challenging to generate feasible joint-space trajectories in HRI environments where safety and kinematic constraints must be considered simultaneously. To address this limitation, collision- and self-collision-aware trajectories are generated using Rapidly-exploring Random Tree (RRT) and RRT* algorithms, and the resulting dataset is used to train a diffusion model that generates constraint-satisfying trajectories through guided sampling. To reduce the inference time required for iterative diffusion sampling, consistency distillation is applied, and a joint-weighted jerk regularization term is incorporated into the loss function to promote smoother trajectories by penalizing abrupt changes in joint acceleration. Simulation results show that the consistency model generates 150 trajectory candidates in less than 100 ms, maintains a high episode success rate, and substantially reduces joint and end-effector jerk when jerk regularization is applied.
