论文标题
通过潜在空间到达：从联合统计到操纵的路径规划
Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation
论文作者
论文摘要
我们提出了一种用于机器人操纵器的路径规划的新方法，其中在机器人姿势的生成模型的潜在空间中，通过迭代优化产生路径。通过使用在同一空间上运行的约束满意度分类器来纳入约束。优化通过我们学到的模型来利用梯度，即使在存在其他不可分割的约束的情况下，也提供了一种简单的方法来将目标实现目标与限制满意度相结合。我们的模型以随机采样的机器人姿势以任务反应的方式进行训练。在与许多广泛使用的计划者的基线比较中，我们在任务成功，计划时间和路径长度方面取得了相应的绩效，并在真正的7-DOF机器人组上避免障碍物进行成功的路径计划。
We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Optimisation leverages gradients through our learned models that provide a simple way to combine goal reaching objectives with constraint satisfaction, even in the presence of otherwise non-differentiable constraints. Our models are trained in a task-agnostic manner on randomly sampled robot poses. In baseline comparisons against a number of widely used planners, we achieve commensurate performance in terms of task success, planning time and path length, performing successful path planning with obstacle avoidance on a real 7-DoF robot arm.
