Title: Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification Show Chinese title

Title: 约束Stein变分梯度下降法在机器人感知、规划和识别中的应用

Abstract: Many core problems in robotics can be framed as constrained optimization problems. Often on these problems, the robotic system has uncertainty, or it would be advantageous to identify multiple high quality feasible solutions. To enable this, we present two novel frameworks for applying principles of constrained optimization to the new variational inference algorithm Stein variational gradient descent. Our general framework supports multiple types of constrained optimizers and can handle arbitrary constraints. We demonstrate on a variety of problems that we are able to learn to approximate distributions without violating constraints. Specifically, we show that we can build distributions of: robot motion plans that exactly avoid collisions, robot arm joint angles on the SE(3) manifold with exact table placement constraints, and object poses from point clouds with table placement constraints. Show Chinese abstract

Abstract: 机器人学中的许多核心问题都可以被表述为约束优化问题。通常，在这些问题上，机器人系统存在不确定性，或者识别多个高质量的可行解会很有优势。为此，我们提出了两种新颖的框架，用于将约束优化的原则应用于新的变分推理算法 Stein 变量梯度下降。我们的通用框架支持多种类型的约束优化器，并能够处理任意约束。我们在各种问题上证明了我们能够学会逼近分布而不违反约束。具体来说，我们展示了如何构建以下分布：完全避免碰撞的机器人运动计划，SE(3) 流形上的机器人手臂关节角度具有精确的桌面放置约束，以及具有桌面放置约束的点云中的对象姿态。