论文标题
预测抽样：与穆乔科的实时行为合成
Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo
论文作者
论文摘要
我们基于Mujoco Physics介绍了Mujoco MPC（MJPC），这是一种用于实时预测控制的开源，交互式应用程序和软件框架。 MJPC允许用户轻松地撰写和解决复杂的机器人技术任务，目前支持三个基于射击的计划者：基于衍生的ILQG和梯度下降，以及我们称为预测性采样的简单无衍生物方法。预测抽样被设计为基础基线，主要是因为其教学价值，但事实证明，与更具成熟的算法相比，具有令人惊讶的竞争力。这项工作没有提出算法的进步，而是优先考虑性能算法，简单的代码和通过直观和交互式软件的基于模型的方法的可访问性。 MJPC可在以下网址找到：github.com/deepmind/mujoco_mpc，可以在以下网址查看视频摘要：dpmd.ai/mjpc。
We introduce MuJoCo MPC (MJPC), an open-source, interactive application and software framework for real-time predictive control, based on MuJoCo physics. MJPC allows the user to easily author and solve complex robotics tasks, and currently supports three shooting-based planners: derivative-based iLQG and Gradient Descent, and a simple derivative-free method we call Predictive Sampling. Predictive Sampling was designed as an elementary baseline, mostly for its pedagogical value, but turned out to be surprisingly competitive with the more established algorithms. This work does not present algorithmic advances, and instead, prioritises performant algorithms, simple code, and accessibility of model-based methods via intuitive and interactive software. MJPC is available at: github.com/deepmind/mujoco_mpc, a video summary can be viewed at: dpmd.ai/mjpc.
