Legged robots navigating cluttered environments must be jointly agile for
efficient task execution and safe to avoid collisions with obstacles or humans.
Existing studies either develop conservative controllers (< 1.0 m/s) to ensure
safety, or focus on agility without considering potentially fatal collisions.
This paper introduces Agile But Safe (ABS), a learning-based control framework
that enables agile and collision-free locomotion for quadrupedal robots. ABS
involves an agile policy to execute agile motor skills amidst obstacles and a
recovery policy to prevent failures, collaboratively achieving high-speed and
collision-free navigation. The policy switch in ABS is governed by a learned
control-theoretic reach-avoid value network, which also guides the recovery
policy as an objective function, thereby safeguarding the robot in a closed
loop. The training process involves the learning of the agile policy, the
reach-avoid value network, the recovery policy, and an exteroception
representation network, all in simulation. These trained modules can be
directly deployed in the real world with onboard sensing and computation,
leading to high-speed and collision-free navigation in confined indoor and
outdoor spaces with both static and dynamic obstacles.

利用一种学习性控制架构，本文介绍了灵巧而安全（ABS）的机器人导航技术，使四足机器人能够以高速且避免碰撞的方式在障碍物多的环境中执行任务。

敏捷但安全：学习无碰撞的高速腿部移动

Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion

Cloud computing and the evolution of management methodologies such as Lean
Management or Agile entail a profound transformation in both system
construction and maintenance approaches. These practices are encompassed within
the term "DevOps." This descriptive approach to an information system or
application, alongside the configuration of its constituent components, has
necessitated the development of descriptive languages paired with specialized
engines for automating systems administration tasks. Among these, the tandem of
Ansible (engine) and YAML (descriptive language) stands out as the two most
prevalent tools in the market, facing notable competition mainly from
Terraform. The current document presents an inquiry into a solution for
generating and managing Ansible YAML roles and playbooks, utilizing Generative
LLMs (Language Models) to translate human descriptions into code. Our efforts
are focused on identifying plausible directions and outlining the potential
industrial applications.
Note: For the purpose of this experiment, we have opted against the use of
Ansible Lightspeed. This is due to its reliance on an IBM Watson model, for
which we have not found any publicly available references. Comprehensive
information regarding this remarkable technology can be found directly on our
partner RedHat's website,
this https URL

利用生成式 LLM 将人类描述转化为代码，本文研究生成和管理 Ansible YAML 角色和 playbooks 的解决方案，聚焦于可行的发展方向和潜在的工业应用。