As machines that are capable of carrying out tasks autonomously, robots have become an integral part of modern-day society. With advancements in technology, robots now have a range of abilities and can perform complex tasks with ease. However, the ability to learn is what sets apart groundbreaking robotics from its predecessors.

Robots require programming to function properly but how do they learn new things? In this article, we will explore how robots learn and acquire new skills through machine learning techniques such as reinforcement learning, deep learning, imitation learning and others.

Reinforcement Learning

Reinforcement Learning

This type of learning involves a robot performing several trials before obtaining precise actions for particular circumstances. Reinforcement signals or feedback are used to direct the behavior of the agent, which learns through trial-and-error methods until it optimizes outcomes.

Reinforcement Learning works best in cases when there is no clear-cut decision on how best to go about executing specific functions as there may exist multiple solutions to a single problem. This technique has been applied successfully in areas ranging from gaming (AlphaGo) – where DeepMind’s AlphaGo mastered complex board games by playing against itself continually- autonomous driving (Tesla Autopilot), industrial robotics applications (ABB’s YuMi), teaching AI agents to walk amongst other tasks.

Deep Learning

Deep Learning

In recent times deep learning has pushed robotic capabilities further than any other algorithmic breakthroughs before us; almost all advancement stems from neural networks inspired by our own biological processes’ structure and functioning. That said training these neural networks requires massive sets of labeled data posing difficulty for implementing self-supervised robot systems since labeling vast amounts isn’t always feasible either financially nor time efficient But promising results continue emerging with incredible feats accomplished like OpenAI’s GPT3 – one-of-a-kind language model capable of exhibiting human-like responses whether written or verbal stimuli provided via various inputs making it likely we’ll see more advanced features implemented into current and future robotic hardware/software stack upon continued research developments.

Imitation Learning

This form of learning mimics the way human beings acquire skills – by copying other people. In robotic applications, imitation learning has been primarily used for programming movements in autonomous machines alongside Reinforcement and deep learning algorithms discussed above providing robustness to machine performance in noisy operational conditions. Robots learn through observation; by being presented with instructional videos or audiovisual recordings associated with particular activities.

The usage of Imitation Learning is very prominent within factory settings & also surgical rooms where robots are programmed to perform tightly controlled maneuvers while leveraging insights from professional surgeons’ prior demonstrations during surgeries enhancing patient safety outcomes along with improved experience, making use of a robotic assistant much simpler than without.

Transfer Learning

In most cases, it’s costly (time-consuming & expensive) developing entirely new neural networks when starting a new task that spans outside beforehand defined expertise domains belonging to pre-trained models; as such Transfer Learning is crucial here allowing us quickly to repurpose preexisting trained models on similar tasks even concerning diverse our application-specific constraints presented.

For example, let’s say we have a robot capable of differentiating between fruits & vegetables via computer vision-based object recognition techniques supervisory teaching methodology used- since labeling datasets may drastically shift depending on what fruit or vegetable needs identifying makes utilizing transfer learning integral so creating a distinct feature extractor element level using already learned features can really aid the training process at large ensuring minimal variations require significant problem-solving time otherwise needed if started& completed from scratch like 95%+ convolutions utilized best-in-class object recognition architectures catered towards ‘Food Classification’.


Robots have continued to demonstrate exceptional capabilities when handling complex operations in various applcations areas which continue expanding beyond factories/surgical environments outlined herein However one thing has remained evident throughout they all require an advanced layering stack onboard so performances remain timely efficient leading ultimately too reduced economic costs for commercial consumers interested investing into these machines’ design/operation strongly emphasizing the importance of machine learning-backed optimization.

As businesses seek to automate more manual work increasingly, robotics will likely play an ever-increasing role within our daily lives shaping how we intermingle with machines or devices. One thing is sure, when thinking about the future of robotics, human-robot collaboration draws attention as both parties grow their capabilities jointly leveraging unique aspects that are simply impossible for one another’s exclusive use.