Preprogrammed offline robots are designed to execute predefined tasks and a fixed set of instructions within a predetermined environment. This means they’re likely to struggle when encountering an unexpected change in their surroundings.
AI-driven, generalized robots can overcome the limitations of preprogrammed robot behaviors. To achieve this, simulation-based robot learning is necessary to enable these robots to perceive, plan, and act autonomously under dynamic conditions.
Robot learning lets these robots gain and refine new capabilities by using robot policies to improve their performance for a variety of scenarios. These policies are learned sets of behaviors—including navigation, dextrous manipulation, locomotion, and many others—that define how a robot should make decisions in various situations.
Benefits of Simulation-Based Robot Learning
Flexibility and Scalability
Iterate, refine, and deploy robot policies for real-world scenarios using various data sources from your real robot-captured data and synthetic data in simulation for any robot embodiment, such as autonomous mobile robots (AMRs), robotic arms, and humanoid robots. The sim-based approach also allows you to quickly train hundreds or thousands of robot instances in parallel.
Accelerated Skill Development
Train robots in simulated environments to adapt to new task variations without the need for reprogramming physical robot hardware.
Physically Accurate Environments
Easily model physical factors like object interactions (rigid or deformables), friction, etc., to significantly reduce the sim-to-real gap.
Safe Proving Environment
Safely test potentially hazardous scenarios without risking human safety or damaging equipment.
Reduce Costs
Avoid the burden of real-world data collection and labeling costs by generating large amounts of synthetic data, validating trained robot policies in simulation and deploying on robots faster.
Robot Learning Algorithms
Robot learning algorithms, such as imitation learning or reinforcement learning, can help robots generalize learned skills, enabling robots to improve their performance with changing or novel environments. There are various learning techniques, including:
- Reinforcement learning: A trial-and-error approach where the robot receives a reward or a penalty based on the actions it takes.
- Imitation learning: The robot can learn from human demonstrations of tasks.
- Supervised learning: The robot can be trained using labeled data to learn specific tasks.
- Diffusion policy: The robot uses generative models to create and optimize robot actions for desired outcomes.
- Self-supervised learning: When there are limited labeled datasets, robots can generate their own training labels from unlabeled data to extract meaningful information.