ニューラルネット全身予測モデルを用いた人間とロボットの協調動作計画
Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models
人間の軌道予測モデルを最適化に組み込み、人間とロボットが互いに適応しながら安全で効率的な動作を計画する手法を提案した。
著者: Philipp Kratzer, Marc Toussaint, Jim Mainprice
分類: cs.RO
原文アブストラクト
Numerical optimization has become a popular approach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efficiency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modifiers to a differentiable human predictive model based on a dedicated recurrent neural network. These modifiers allow to change the human prediction within motion optimization. We empirically evaluate our method using the publicly available MoGaze dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other.
