论文标题
学习机器人轨迹受运动限制的约束
Learning Robot Trajectories subject to Kinematic Joint Constraints
论文作者
论文摘要
我们提出了一种学习快速和动态的机器人运动的方法，而不会超过位置$θ$，速度$ \dotθ$，加速度$ \ddotθ$和每个机器人关节的混蛋$ \dddotθ$。通过将神经网络的预测映射到安全可执行的关节加速度来产生运动。定期调用神经网络，并通过增强学习进行培训。我们的主要贡献是计算安全关节加速度的分析程序，该程序考虑了神经网络的预测频率$ f_n $。结果，频率$ f_n $可以自由选择并将其视为超参数。我们表明，我们的方法比对约束违规行为进行惩罚，因为它提供了明确的保证，并且不会扭曲所需的优化目标。此外，各种实验强调了所选预测频率对学习绩效和计算工作的影响。
We present an approach to learn fast and dynamic robot motions without exceeding limits on the position $θ$, velocity $\dotθ$, acceleration $\ddotθ$ and jerk $\dddotθ$ of each robot joint. Movements are generated by mapping the predictions of a neural network to safely executable joint accelerations. The neural network is invoked periodically and trained via reinforcement learning. Our main contribution is an analytical procedure for calculating safe joint accelerations, which considers the prediction frequency $f_N$ of the neural network. As a result, the frequency $f_N$ can be freely chosen and treated as a hyperparameter. We show that our approach is preferable to penalizing constraint violations as it provides explicit guarantees and does not distort the desired optimization target. In addition, the influence of the selected prediction frequency on the learning performance and on the computing effort is highlighted by various experiments.
