Opening up a physics simulator for robotics

开发机器人物理模拟器

Advancing research everywhere with the acquisition of MuJoCo

随着MuJoCo的收购，到处推进研究

When you walk, your feet make contact with the ground. When you write, your fingers make contact with the pen. Physical contacts are what makes interaction with the world possible. Yet, for such a common occurrence, contact is a surprisingly complex phenomenon. Taking place at microscopic scales at the interface of two bodies, contacts can be soft or stiff, bouncy or spongy, slippery or sticky. It’s no wonder our fingertips have four different types of touch-sensors. This subtle complexity makes simulating physical contact — a vital component of robotics research — a tricky task.

当你走路时，你的脚与地面接触。当你写字时，你的手指与笔接触。身体接触使与世界的互动成为可能。然而，对于这样一种常见的现象，接触却是一种令人惊讶的复杂现象。在微观尺度下发生在两个物体的界面，触点可以是软的或硬的，有弹性的或海绵状的，滑的或黏的。难怪我们的指尖有四种不同的触摸传感器。这种微妙的复杂性使得模拟身体接触——机器人研究的一个重要组成部分——成为一项棘手的任务。

The rich-yet-efficient contact model of the MuJoCo physics simulator has made it a leading choice by robotics researchers and today, we're proud to announce that, as part of DeepMind's mission of advancing science, we've acquired MuJoCo and are making it freely available for everyone, to support research everywhere. Already widely used within the robotics community, including as the physics simulator of choice for DeepMind’s robotics team, MuJoCo features a rich contact model, powerful scene description language, and a well-designed API. Together with the community, we will continue to improve MuJoCo as open-source software under a permissive licence. As we work to prepare the codebase, we are making MuJoCo freely available as a precompiled library.

MuJoCo物理模拟器丰富而高效的接触模型使其成为机器人研究人员的首选。今天，我们自豪地宣布，作为DeepMind推进科学使命的一部分，我们已经收购了MuJoCo，并将其免费提供给每个人，以支持各地的研究。MuJoCo已经在机器人社区中广泛使用，包括作为DeepMind机器人团队的物理模拟器的选择，MuJoCo具有丰富的接触模型、强大的场景描述语言和设计良好的API。与社区一起，我们将继续改进MuJoCo作为开放源码软件的许可许可。在我们准备代码库时，我们将MuJoCo作为预编译库免费提供。

A balanced model of contact. MuJoCo, which stands for Multi-Joint Dynamics with Contact, hits a sweet spot with its contact model, which accurately and efficiently captures the salient features of contacting objects. Like other rigid-body simulators, it avoids the fine details of deformations at the contact site, and often runs much faster than real time. Unlike other simulators, MuJoCo resolves contact forces using the convex Gauss Principle. Convexity ensures unique solutions and well-defined inverse dynamics. The model is also flexible, providing multiple parameters which can be tuned to approximate a wide range of contact phenomena.

一种平衡的接触模式。MuJoCo是Multi-Joint Dynamics with Contact的缩写，它的接触模型精确而有效地捕捉了接触物体的显著特征，达到了最佳效果。与其他刚体模拟器一样，它避免了接触点变形的细节，运行速度通常比实时快得多。与其他模拟器不同，MuJoCo使用凸高斯原理解决接触力。凸性保证了唯一解和定义良好的逆动力学。该模型也很灵活，提供了多个参数，可以调整以近似广泛的接触现象。

Real physics, no shortcuts. Because many simulators were initially designed for purposes like gaming and cinema, they sometimes take shortcuts that prioritise stability over accuracy. For instance, they may ignore gyroscopic forces or directly modify velocities. This can be particularly harmful in the context of optimisation: as first observed by artist and researcher Karl Sims, an optimising agent can quickly discover and exploit these deviations from reality. In contrast, MuJoCo is a second-order continuous-time simulator, implementing the full Equations of Motion. Familiar yet non-trivial physical phenomena like Newton’s Cradle, as well as unintuitive ones like the Dzhanibekov effect, emerge naturally. Ultimately, MuJoCo closely adheres to the equations that govern our world.

真正的物理，没有捷径。因为许多模拟器最初是为游戏和电影等目的设计的，它们有时会走捷径，优先考虑稳定性而不是准确性。例如，它们可能忽略陀螺力或直接修改速度。这在优化的情况下尤其有害:正如艺术家和研究人员Karl Sims首先观察到的那样，优化代理可以迅速发现和利用这些与现实的偏差。相比之下，MuJoCo是一个二阶连续时间模拟器，实现完整的运动方程。熟悉而又不平凡的物理现象，如牛顿的摇篮，以及不直观的Dzhanibekov效应，都自然而然地出现了。最终，MuJoCo紧紧遵循着统治我们世界的方程式。

Portable code, clean API. MuJoCo’s core engine is written in pure C, which makes it easily portable to various architectures. The library produces deterministic results, with the scene description and simulation state fully encapsulated within two data structures. These constitute all the information needed to recreate a simulation, including results from intermediate stages, providing easy access to the internals. The library also provides fast and convenient computations of commonly used quantities, like kinematic Jacobians and inertia matrices.

可移植的代码，干净的API。MuJoCo的核心引擎是用纯C编写的，这使得它很容易移植到各种体系结构上。该库产生确定性的结果，场景描述和模拟状态完全封装在两个数据结构中。这些构成了重新创建模拟所需的所有信息，包括来自中间阶段的结果，提供了对内部的方便访问。该库还提供了常用量的快速和方便的计算，如运动学雅可比矩阵和惯性矩阵。

Powerful scene description. The MJCF scene-description format uses cascading defaults — avoiding multiple repeated values ​​— and contains elements for real-world robotic components like equality constraints, motion-capture markers, tendons, actuators, and sensors. Our long-term roadmap includes standardising MJCF as an open format, to extend its usefulness beyond the MuJoCo ecosystem.

强大的场景描述。MJCF场景描述格式使用级联默认值—避免多个重复值—并包含真实机器人组件的元素，如相等约束、动作捕捉标记、肌腱、执行器和传感器。我们的长期路线图包括将MJCF标准化为一种开放格式，将其用途扩展到MuJoCo生态系统之外。

Biomechanical simulation. MuJoCo includes two powerful features that support musculoskeletal models of humans and animals. Spatial tendon routing, including wrapping around bones, means that applied forces can be distributed correctly to the joints, describing complicated effects like the variable moment-arm in the knee enabled by the tibia. MuJoCo’s muscle model captures the complexity of biological muscles, including activation states and force-length-velocity curves.

生物力学模拟。MuJoCo包含两个强大的功能，支持人类和动物的肌肉骨骼模型。空间肌腱路线，包括缠绕在骨头上，意味着施加的力量可以正确地分配到关节，描述复杂的影响，如膝关节由胫骨实现的可变力矩臂。MuJoCo的肌肉模型捕捉了生物肌肉的复杂性，包括激活状态和力-长度-速度曲线。

A recent PNAS perspective exploring the state of simulation in robotics identifies open source tools as critical for advancing research. The authors’ recommendations are to develop and validate open source simulation platforms as well as to establish open and community-curated libraries of validated models. In line with these aims, we’re committed to developing and maintaining MuJoCo as a free, open-source, community-driven project with best-in-class capabilities. We’re currently hard at work preparing MuJoCo for full open sourcing, and we encourage you to download the software from the new homepage and visit the GitHub repository if you'd like to contribute. Email us if you have any questions or suggestions, and if you're also excited to push the boundaries of realistic physics simulation, we're hiring. We can’t promise we’ll be able to address everything right away, but we’re eager to work together to make MuJoCo the physics simulator we’ve all been waiting for.

最近PNAS对机器人仿真现状的探索表明，开源工具对于推进研究至关重要。作者的建议是开发和验证开源仿真平台，并建立开放和社区策划的验证模型库。根据这些目标，我们致力于将MuJoCo开发和维护为一个免费的、开源的、社区驱动的、具有一流功能的项目。我们目前正在努力使MuJoCo完全开源，我们鼓励您从新的主页下载该软件，并访问GitHub存储库，如果您想投稿的话。如果您有任何问题或建议，请发送电子邮件给我们，如果您也对推动现实物理模拟的边界感到兴奋，我们正在招聘。我们不能保证能够立即解决所有问题，但我们渴望共同努力，使MuJoCo成为我们期待已久的物理模拟器。

MuJoCo in DeepMind. Our robotics team has been using MuJoCo as a simulation platform for various projects, mostly via our dm_control Python stack. In the carousel below, we highlight a few examples to showcase what can be simulated in MuJoCo. Of course, these clips represent only a tiny fraction of the vast possibilities for how researchers might use the simulator. For higher quality versions of these clips, please click here.

MuJoCo DeepMind。我们的机器人团队一直在使用MuJoCo作为各种项目的模拟平台，主要是通过我们的dm_control Python堆栈。在下面的旋转木马中，我们重点介绍了几个示例，以展示在MuJoCo中可以模拟什么。当然，这些片段只是研究人员如何使用模拟器的巨大可能性中的一小部分。如欲观看更高质量的影片，请点击这里。