Accurate control of robots at high speeds requires a control system that can
take into account the kinodynamic interactions of the robot with the
environment. Prior works on learning inverse kinodynamic (IKD) models of robots
have shown success in capturing the complex kinodynamic effects. However, the
types of control problems these approaches can be applied to are limited only
to that of following pre-computed kinodynamically feasible trajectories. In
this paper we present Optim-FKD, a new formulation for accurate, high-speed
robot control that makes use of a learned forward kinodynamic (FKD) model and
non-linear least squares optimization. Optim-FKD can be used for accurate, high
speed control on any control task specifiable by a non-linear least squares
objective. Optim-FKD can solve for control objectives such as path following
and time-optimal control in real time, without needing access to pre-computed
kinodynamically feasible trajectories. We empirically demonstrate these
abilities of our approach through experiments on a scale one-tenth autonomous
car. Our results show that Optim-FKD can follow desired trajectories more
accurately and can find better solutions to optimal control problems than
baseline approaches.

本研究提出了 Optim-FKD，一种新的机器人高速控制的公式，该公式利用学习的前向动力学（FKD）模型和非线性最小二乘优化，在实时控制任务中可以解决难以预测的动态影响。该公式在小型汽车上的实验结果表明，相比于基准方法，Optim-FKD 可以更精确地跟踪所需轨迹，并找到更好的最优控制解决方案。

利用学习的正运动学动力学和非线性最小二乘优化进行高速准确机器人控制

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization

Accurate and reliable tracking of multiple moving objects in 3D space is an
essential component of urban scene understanding. This is a challenging task
because it requires the assignment of detections in the current frame to the
predicted objects from the previous one. Existing filter-based approaches tend
to struggle if this initial assignment is not correct, which can happen easily.
We propose a novel optimization-based approach that does not rely on explicit
and fixed assignments. Instead, we represent the result of an off-the-shelf 3D
object detector as Gaussian mixture model, which is incorporated in a factor
graph framework. This gives us the flexibility to assign all detections to all
objects simultaneously. As a result, the assignment problem is solved
implicitly and jointly with the 3D spatial multi-object state estimation using
non-linear least squares optimization. Despite its simplicity, the proposed
algorithm achieves robust and reliable tracking results and can be applied for
offline as well as online tracking. We demonstrate its performance on the real
world KITTI tracking dataset and achieve better results than many
state-of-the-art algorithms. Especially the consistency of the estimated tracks
is superior offline as well as online.

本文提出了基于高斯混合模型和因子图框架的非线性最小二乘优化，用于处理三维空间中多个移动物体的跟踪问题，并展示了在 KITTI 跟踪数据集上实验的表现。