论文标题
一组凸原语的可区分碰撞检测
Differentiable Collision Detection for a Set of Convex Primitives
论文作者
论文摘要
物体之间的碰撞检测对于机器人系统的模拟，控制和学习至关重要。但是，现有的碰撞检测例程本质上是不可差异的，这限制了其在基于梯度的优化工具中的应用。在这项工作中，我们提出了DCOL：一个快速且完全可分化的碰撞检测框架，原因是一组可合理和高度表达的凸原始形状之间的碰撞。这是通过将碰撞检测问题制定为凸优化问题来实现的，该问题解决了在每个原始物体相交之前应用于每个原始的最小均匀缩放的问题。相对于每个原始性的配置，优化问题是完全可区分的，并且能够返回每个对象上的碰撞检测指标和接触点，即互穿的不可知论。我们演示了DCOL在轨迹优化和接触物理学的一系列机器人问题上的功能，并可以提供开源实现。
Collision detection between objects is critical for simulation, control, and learning for robotic systems. However, existing collision detection routines are inherently non-differentiable, limiting their applications in gradient-based optimization tools. In this work, we propose DCOL: a fast and fully differentiable collision-detection framework that reasons about collisions between a set of composable and highly expressive convex primitive shapes. This is achieved by formulating the collision detection problem as a convex optimization problem that solves for the minimum uniform scaling applied to each primitive before they intersect. The optimization problem is fully differentiable with respect to the configurations of each primitive and is able to return a collision detection metric and contact points on each object, agnostic of interpenetration. We demonstrate the capabilities of DCOL on a range of robotics problems from trajectory optimization and contact physics, and have made an open-source implementation available.
