What is Robot Learning?
Robot learning uses machine learning and AI to help robots acquire new skills and adapt to novel situations, rather than relying solely on pre-programmed instructions.
According to NVIDIA, it's "a collection of algorithms and methodologies that help a robot learn new skills such as manipulation, locomotion, and classification in either a simulated or real-world environment."2
Robot learning sits at the intersection of robotics and artificial intelligence (AI). Robot learning systems often use techniques from control theory (for stable movement), computer vision (for perception of objects and environments), reinforcement learning (for trial-and-error decision-making), and even neuroscience-inspired methods (neural network designs modeled on the brain).
In practice, this means robots can learn tasks such as walking, grasping objects, recognizing items, or following spoken instructions by processing data and feedback, rather than just executing fixed code.
Two Approaches
Classical Robotics
Robots follow pre-set sequences and work well in controlled environments. They struggle with changes or variations.
- Pre-programmed instructions
- Fixed routines
- Limited adaptability
As NVIDIA notes, pre-programmed robots "succeeded in predefined environments but struggled with new disturbances or variations and lacked the robustness needed for dynamic real-world applications."2
Robot Learning
Robots learn from data and adapt to changing conditions. They can acquire novel skills over time.
- Learn from demonstrations
- Adapt to new situations
- Handle uncertainty
Learning-based robots use data to adapt: they can be trained on examples or through trial-and-error so that if a part shifts slightly or lighting changes, they can adjust their behavior.
Key Techniques
Robot learning combines several key techniques to enable adaptive, intelligent behavior:
Control Theory
Enables stable movement and precise manipulation through mathematical models of motion and force.
Computer Vision
Provides perception of objects and environments, allowing robots to "see" and understand their surroundings.
Reinforcement Learning
Allows trial-and-error decision-making where robots learn from rewards and penalties to optimize behavior. This approach enables robots to discover effective strategies through interaction and exploration rather than relying on labor-intensive programming.
Neural Networks
Create brain-inspired learning systems that can recognize patterns and make complex decisions.
Key Application Areas
Robot learning enables capabilities across several critical domains:
Manipulation & Dexterous Tasks
Robots learn to grasp, manipulate, and handle objects with precision—from picking items in warehouses to performing delicate assembly tasks. Dexterous manipulation allows robots to work with complex objects in unstructured environments.
Locomotion & Navigation
Learning-based approaches enable robots to navigate complex, dynamic environments—from hospital corridors to outdoor terrain. Robots can adapt their movement strategies based on the surfaces and obstacles they encounter.
Real-World Environments
Unlike classical robots confined to controlled factory floors, learning-based robots can operate in human-centered spaces—hospitals, homes, offices—where conditions are unpredictable and constantly changing.
Learning from Demonstration
Robots can observe human experts performing tasks and learn to replicate those behaviors—dramatically reducing the time and expertise required to program new capabilities.
"Robotics is undergoing a major transformation in scope and dimension with increasing impact on the economy, production, and culture of our global society."
— Stanford Robotics Center1
When we think about technology's impact, we often focus on the digital realm. But the physical world also constrains human capabilities—and that's where robotics holds transformative promise.
About Sidekick Robotics
Sidekick Robotics is creating the AI Sidekick for Physical Work, robots that learn and perform the complex tasks that keep hospitals and care communities running. We're tackling the healthcare labor shortage by reducing burnout, turnover, and rising costs, so healthcare workers can focus on what matters most: patients.
Media Contact: founders@sidekickrobotics.ai