论文标题
trueædapt：在关节空间中学习流畅的在线轨迹适应，加速和速度
TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space
论文作者
论文摘要
我们提出了Trueædapt，这是一种根据其对环境的影响的机器人轨迹在线改编的无模型方法。鉴于原始轨迹的感觉反馈和未来的路点，对神经网络进行了训练，以定期预测关节加速度。适应的轨迹是通过预测加速度的线性插值产生的，导致连续可区分的关节速度和位置。通过在每个决策步骤中计算有效加速度的范围并相应地剪切网络的输出，可以保证有界的混蛋，加速度和速度。在训练过程中的偏差罚款会导致改编的轨迹遵循原始轨迹。通过惩罚高加速度和混蛋来鼓励流畅的动作。我们通过训练模拟的Kuka IIWA机器人来评估我们的方法，以平衡板上的球，同时移动并证明可以将平衡策略直接转移到真正的机器人中。
We present TrueÆdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuously differentiable joint velocities and positions. Bounded jerks, accelerations and velocities are guaranteed by calculating the range of valid accelerations at each decision step and clipping the network's output accordingly. A deviation penalty during the training process causes the adapted trajectory to follow the original one. Smooth movements are encouraged by penalizing high accelerations and jerks. We evaluate our approach by training a simulated KUKA iiwa robot to balance a ball on a plate while moving and demonstrate that the balancing policy can be directly transferred to a real robot.
