TrueRMA：在笛卡尔空间中使用递归中点自适应学习快速平滑的机器人轨迹
TrueRMA: Learning Fast and Smooth Robot Trajectories with Recursive Midpoint Adaptations in Cartesian Space
摘要：
我们提出了TrueRMA，这是一种数据高效，无模型的方法，可以在广泛的起点和终点上学习成本优化的机器人轨迹。其关键思想是通过递归预测相对于直线中点的正交适应来计算笛卡尔空间中的轨迹航路点。我们通过在路径点周围添加圆形混合物来生成可微路径，使用逆运动学求解器计算相应的关节位置，并考虑速度和加速度限制来计算时间最优参数化。在训练期间，在物理模拟器中执行轨迹，并且根据用户指定的成本函数分配成本，该成本函数不需要是可微的。给定起始点和终点作为输入，训练神经网络以预测中点适应性，该中点适应性通过强化学习使所得轨迹的成本最小化。我们成功地训练了一台KUKA iiwa机器人，使其在指定点之间移动时将球保持在盘子上，并将TrueRMA的性能与两个基线进行了比较。结果表明，我们的方法需要更少的训练数据来学习任务，同时生成更短，更快的轨迹。
英文摘要：
We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively predicting orthogonal adaptations relative to the midpoints of straight lines. We generate a differentiable path by adding circular blends around the waypoints, calculate the corresponding joint positions with an inverse kinematics solver and calculate a time-optimal parameterization considering velocity and acceleration limits. During training, the trajectory is executed in a physics simulator and costs are assigned according to a user-specified cost function which is not required to be differentiable. Given a starting point and an endpoint as input, a neural network is trained to predict midpoint adaptations that minimize the cost of the resulting trajectory via reinforcement learning. We successfully train a KUKA iiwa robot to keep a ball on a plate while moving between specified points and compare the performance of TrueRMA against two baselines. The results show that our method requires less training data to learn the task while generating shorter and faster trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
