机器学习在机器人技术中的应用 —— Machine Learning Applications in Robotics

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https://www.wandelbots.com/blog/machine-learning-in-robotics

机器学习在机器人技术中的应用

机器人视觉系统
机器人视觉系统（也称为机器视觉）将传感器和摄像头与机器学习算法结合使用。这些传感器和摄像头采集物理数据，机器学习算法处理这些数据，从而使机器人能够感知和理解环境。

模仿学习
模仿学习的目标是为机器人建立一种控制策略，将机器人的状态映射到相应的动作上。演示通常以状态-动作轨迹的形式表示，并作为学习系统的输入。例如，用户可以向机器人演示一个特定动作，机器人随后会重复执行该动作，并根据当前环境进行适应。

机器人基础模型
机器人基础模型类似于大型语言模型（LLMs），是通过在大规模多样化的数据集上训练的深度神经网络。这些模型能够找到“零样本”解决方案，即解决数据中未明确表示的任务。通过将感知、决策和控制整合到一个模型中，基础模型为解决复杂任务提供了一种有前途的方法。

多智能体强化学习 (MARL)
在机器人技术中，多智能体强化学习的关键在于协调与协商。这种机器学习的应用使机器人能够建立环境的数据目录，并交叉引用其他机器人的数据日志，从而创建其环境和行为的全面知识库。这种应用主要适用于移动机器人。

机器学习的挑战
与任何技术一样，机器学习也有优劣之处。机器学习需要大量的前期投资，这可能限制了中小型企业的实施。此外，部署机器学习算法会增加系统的复杂性，并需要持续的维护和更新。然而，尽管存在缺点，机器学习的优势远远超过其劣势，使其成为制造业中具有前瞻性和创新性的卓越投资。

结论
机器学习是正在变革各个行业的众多技术之一，凭借机器人能力的提升，它推动了数字化制造的浪潮。采用这项技术使制造商在效率、精确度和适应性方面占据优势。随着制造业的不断发展，机器学习与机器人技术的动态交互将成为创新的核心驱动力，推动行业迈向更加自动化和智能化的未来。

Machine Learning Applications in Robotics
Now that we better understand machine learning, let's explore its applications in robotics. Note that some of these concepts are still being researched and require more testing before we fully understand their scope.

Robot Vision Systems: Also known as machine vision, robot vision systems integrate sensors and cameras that take in physical data and machine learning algorithms that can then process the data.

Imitation Learning: Imitation learning aims to establish a robot control policy that maps a robot's states to actions. Demonstrations are represented as state-action trajectories and work as input for the learning system. One could for instance demonstrate a robot a specific action which then gets re-executed again and again including adoptions to the current environment.

Robot Foundation Models: Similar to Large-Language-Models (LLMs) robot foundation models are deep neural networks trained on massive and diverse data sets giving the potential to find zero-shot solutions which means solving tasks not represented in the data. By combining perception, decision-making and control within one model, foundation models provide a promising way to solve complex tasks.

Multi-Agent Reinforcement Learning (MARL): The vital components of MARL in robots are coordination and negotiation. This application of machine learning allows robots to build the data catalogs of their environment and then cross-reference other robotic data logs to create a comprehensive knowledge base of their environment and actions. This application is mostly relevant for mobile robotics.

Challenges in Machine Learning
Like any technology, machine learning has pros and cons. Machine learning requires a significant initial investment, which can limit the implementation of ML technology to only larger companies with the funds. Deploying machine learning algorithms also adds a significant layer of complexity and requires ongoing maintenance and updates. Though there are cons, the pros far outweigh them, making it an excellent investment for forward thinkers and innovators in manufacturing.

Conclusion
Machine learning is one technology on a long list that is transforming industries and bringing a wave of digital manufacturing with the enhancement of robot capabilities. Embracing this technology gives manufacturers an edge in efficiency, precision, and adaptability. As manufacturing continues to evolve, the dynamic between machine learning and robotics will be a fundamental driver of innovation, moving the industry forward to a more automated and smart future.