The field of robotics has made significant advances in recent years, with every day bringing new and fascinating applications. One application that has caught the attention of many researchers is the use of machine learning approaches to control robots.
This blog post starts by defining what machine learning is, how it works, and what it does for roboticists.
It then discusses various strategies for controlling robot behavior using machine learning techniques, including an overview of state-of-the-art reinforcement learning techniques that have been employed in a number of robotics settings.
Finally, it presents a number of future research directions that may expand the capabilities of machine learning in robotics.
What Is Machine Learning For Robot Control
Machine Learning (ML) is a branch of Artificial Intelligence that uses data and algorithms to help make decisions, finds patterns in data and then learns from those patterns to achieve desirable results. The application of machine learning in Robotics is mostly through Reinforcement Learning.
Reinforcement Learning refers to a class of algorithms that learn control policies based on observations of their agent’s behavior, which can be real-time sensor input or simulated robot sensors, within a certain environment.
Only 15% of firms now employ AI, but 31% intend to do so in the upcoming 12 months.
Reinforcement learning refers to a class of algorithms that learn control policies based on observations of their agent’s behavior, which can be real-time sensor input or simulated robot sensors, within a certain environment.
Current Applications In Robotics Industry
The most widespread applications of machine learning in robotics are in self-driving cars and remote-operated drones. Self-driving cars use deep machine learning algorithms based on ANNs (Artificial Neural Networks) for tasks such as object detection and pedestrian identification.
DARPA (Defense Advanced Research Projects Agency) has funded several research projects regarding this application.
DARPA’s “Trax” program is an example of a robotic platform that uses machine learning to improve situational awareness for the operator. DARPA’s “Journey” program is also exploring the use of machine learning in self-driving cars, by using it for classification tasks: recognizing nearby objects and pedestrians, determining their distance from the vehicle, and estimating their trajectories.
The market for industrial robotics is anticipated to increase by 175% over the following ten years, according to Loup Ventures study.
Machine learning is also being used in autonomous drone applications where it can help with path planning (by using algorithms such as AHP or AHP+), collision avoidance, flight control and even navigation.
Benefits Of Machine Learning For Robot Control
Model predictive control is a control technique that uses a dynamic model of the system to make predictions about the future behavior of the system.
Deep reinforcement learning is a neural network technique that can be used to learn a control policy for the system. Predictive control is a control technique that uses the predictions of the system’s behavior to control the system.
A humanoid robot is a machine that is designed to look and act like a human. There are many different types of humanoid robots, but they all share some basic features, such as a head, two arms, and two legs.
1. Fast Adaptation
Unlike traditional programming methods, machine learning can adapt to changes in real-time and quickly learns to perform complex tasks through adaptation and repetition.
Machine learning algorithms typically don’t have to be trained with a set of known rules and can perform using only observed data, which is why they are also called unsupervised Learning algorithms.
This means that there are no underlying assumptions about the problem being modeled or how it should be solved. This characteristic makes it excellent for solving tasks where experience is limited, such as self-driving cars.
2. Flexibility & Customization
ML techniques are capable of adapting their behaviors based on the inputs received from the environment and/or received from other agents (humans) for instance.
This characteristic makes the approach more flexible and beneficial than a traditional programming approach, as it’s possible in many cases to create customized solutions for specific problems.
According to the same Loup analysis, 34% of industrial robots sold by 2025 will be collaborative, meaning they’ll be made to function safely alongside people in plants and factories.
3. Can Deal With Variables That Can Change Over Time
Machine learning algorithms can be trained to deal with variables that can change over time based on new data. Training data is used to modify the model’s parameters in order to make accurate predictions over a set of inputs.
For example, machine learning algorithms can adapt to changing lighting conditions by using analysis of images from cameras or satellite imagery. These algorithms are then used for applications such as surveillance or autonomous driving.
4. Ability To Learn From Simulations
Machine learning algorithms can be used to learn from simulations instead of from hard data. They are also capable of learning new concepts that cannot be directly observed.
This characteristic is generally referred to as unsupervised machine learning and can be used for applications such as self-driving vehicles and robot control systems.
5. Expertise Can Be Shared Throughout The World
Algorithms trained using data from many robots can be shared with other users, greatly simplifying their development effort by saving them the time and pain of repeating the same work for different robots.
This characteristic is particularly useful for distribution of training data, which can include pictures and other visual representations.
According to the Robotic Industries Association, there were 32% more robot purchases in North America in the first quarter of 2017 compared to the same period in 2016.
6. Constructive Criticism
Machine learning algorithms are able to learn faults or incorrect behavior that may occur in their operation. This characteristic is particularly useful when dealing with unsupervised learning algorithms, as the agent gathers information it can never obtain without feedback from other agents (humans).
Examples of this type of critic include collaborative robots that collaborate with humans. As a result, any mistakes made by the human are automatically corrected by the robot’s behavior.
This characteristic can also be used to make improvements on a specific robot’s behavior through experimentation, analysis and adjustments to its parameters.
7. Applications Are Wide And Multifaceted
One of the most significant advantages of machine learning for robot control is its ability to be applied in a wide range of applications. This characteristic allows for rapid adaptation to changes in the environment, such as self-driving vehicles that use machine learning to deal with unforeseen events.
Examples of this type of application include the human-robot communication architecture that has been created by researchers at the University of Pennsylvania (U.S.). Other applications include control of remotely operated aerial drones, which are becoming increasingly popular in many fields including search and rescue operations and land surveys.
8. More Robust
Machine learning algorithms are able to leverage their experience and knowledge of large datasets to guide their decisions. This characteristic makes the approach more robust than an older programming paradigm that relies on a set of predefined rules.
Applications that have benefited from this characteristic include self-driving cars, robots for surveillance, and autonomous drones.
Risks Of Machine Learning For Robot Control
Humanoid robots are often used in research and development because they can be used to test new model based methods and reinforcement learning algorithms. Additionally, humanoid robots can be used for control tasks in mechanical engineering and other industries.
According to Zew, between 1999 and 2010, automation increased employment in Europe.
Deep learning is a type of machine learning that is used to track performance and physical data.
One example of deep learning is using a robot arm to track the movement of a person. This type of learning is important for machines because it allows them to intelligence.
1. Lack Of Consistent Validation
As machine learning algorithms have been widely adopted, it is widely applied to many tasks in robotics where validation has not been performed before. This lack of performance testing for the algorithms can be a cause for concern.
2. Lack Of Informed Consent
Researchers and developers are unaware of the potential risks associated with using machine learning algorithm applications prior to use. As a result, they deploy the technology without having validated its functionality and without understanding it fully.
Even worse, users may not realize the potential dangers associated with these applications until they are used in practice, or fail to recognize issues when they do occur.
3. Control Of Robot Behaviors
The problem of control of robot behavior is at the core of machine learning for robot control. Although practical applications using this technology are available, there are still critical issues that remain to be solved (e.g. how tools and platforms can be developed to enable understanding and monitoring).
As a result, there is a serious risk that these algorithms may cause unexpected behavior in robots that were not foreseen by designers or developers.
4. Outdated Training Datasets May Cause Misbehavior
Training datasets that are outdated may also cause misbehavior in robots and other applications which use machine learning for training datasets (e.g., machine vision training datasets).
As a result, it is important to define a new training dataset at the beginning of a machine learning project so that the algorithm will be adjusted to changes in the environment (e.g., increasing pedestrian traffic).
5. Risk Of Unsupervised Learning
Unsupervised learning is one of the most popular uses of machine learning for robot control, which raises questions regarding how robots can be customized according to information gathered by these algorithms (e.g., assume that many people will have different preferences when it comes to surveillance).
Furthermore, behavior modeling and simulation can cause risk as well as provide an opportunity for hackers or other intruders (e.g., inappropriate decision making). Machine learning also poses risks associated with potential violation of privacy law.
6. Lack Of Technical Support
Because the implementation of these applications is not well-understood, it is difficult for developers to provide technical support for those who choose to use these applications.
This lack of support results in less awareness of the risks involved in machine learning algorithms and poor user testing on the part of robots and other devices.
By 2020, ABI Research projects that the collaborative robotics market will grow to $1 billion in total revenue, with the introduction of over 40,000 cobots.
7. Privacy Concerns Related To Security
Machine learning algorithms use information that is sensitive or private, such as personally identifiable information or credit card numbers, which poses a potential threat for security concerns.
More research is required regarding how this technology will affect people’s behavior without risk (e.g., whether people will take more risks when using a robot that uses machine learning).
8. Lack Of Robust Self-Learning
There is not a single algorithm that can be used by a robot or other machine learning machine application to learn and adapt over time.
In addition, there are no common models that can be used by various machine learning algorithms for the same task, making it difficult for users to understand the behavior of the applications using machine learning.
Machine learning is still in its infancy when it comes to robotics and related fields such as drones, drones cameras, and drones without camera.
However, there are many applications still in need of improvements before they can provide useful services to people outside research labs or schools (i.e. they need to be better-tested).
To make this happen, we will need greater cooperation (e.g., training datasets, different algorithms for the same tasks) and more research in the field of robotics.