Robots are moving goods in warehouses, packaging foods and helping assemble vehicles - when they're not flipping burgers or serving lattes.
How did they get so skilled so fast? Robotics simulation.
Making leaps in progress, it's transforming industries all around us.
Robotics Simulation Summarized A robotics simulator places a virtual robot in virtual environments to test the robot's software without requiring the physical robot. And the latest simulators can generate datasets to be used to train machine learning models that will run on the physical robots.
In this virtual world, developers create digital versions of robots, environments and other assets robots might encounter. These environments can obey the laws of physics and mimic real-world gravity, friction, materials and lighting conditions.
Who Uses Robotics Simulation? Robots boost operations at massive scale today. Some of the biggest and most innovative names in robots rely on robotics simulation.
Fulfillment centers handle tens of millions of packages a day, thanks to the operational efficiencies uncovered in simulation.
Amazon Robotics uses it to support its fulfillment centers. BMW Group taps into it to accelerate planning for its automotive assembly plants. Soft Robotics applies it to perfecting gripping for picking and placing foods for packaging.
Automakers worldwide are supporting their operations with robotics.
Car companies employ nearly 14 million people. Digitalization will enhance the industry's efficiency, productivity and speed, said NVIDIA CEO Jensen Huang during his latest GTC keynote.
How Robotics Simulation Works, in Brief An advanced robotics simulator begins by applying fundamental equations of physics. For example, it can use Newton's laws of motion to determine how objects move over a small increment of time, or a timestep. It can also incorporate physical constraints of a robot, such as being composed of hinge-like joints, or being unable to pass through other objects.
Simulators use various methods to detect potential collisions between objects, identify contact points between colliding objects, and compute forces or impulses to prevent objects from passing through one another. Simulators can also compute sensor signals sought by a user, such as torques at robot joints or forces between a robot's gripper and an object.
The simulator will then repeat this process for as many timesteps as the user requires. Some simulators - such as NVIDIA Isaac Sim, an application built on NVIDIA Omniverse - can also provide physically accurate visualizations of the simulator output at each timestep.
Using a Robotics Simulator for Outcomes A robotics simulator user will typically import computer-aided design models of the robot and either import or generate objects of interest to build a virtual scene. A developer can use a set of algorithms to perform task planning and motion planning, and then prescribe control signals to carry out those plans. This enables the robot to perform a task and move in a particular way, such as picking up an object and placing it at a target location.
The developer can observe the outcome of the plans and control signals and then modify them as needed to ensure success. More recently, there's been a shift toward machine learning-based methods. So instead of directly prescribing control signals, the user prescribes a desired behavior, like moving to a location without colliding. In this situation, a data-driven algorithm generates control signals based on the robot's simulated sensor signals.
These algorithms can include imitation learning, in which human demonstrations can provide references, and reinforcement learning, where robots learn to achieve behaviors through intelligent trial-and-error, achieving years of learning quickly with an accelerated virtual experience.
Simulation Drives Breakthroughs Simulation solves big problems. It is used to verify, validate and optimize robot designs and systems and their algorithms. Simulation also helps design facilities to be optimized for maximum efficiencies before construction or remodeling begins. This helps to reduce costly manufacturing change orders.
For robots to work safely among people, flawless motion planning is necessary. To handle delicate objects, robots need to be precise at making contact and grasping. These machines, as well as autonomous mobile robots and vehicle systems, are trained on vast sums of data to develop safe movement.
Drawing on synthetic data, simulations are enabling virtual advances that weren't previously possible. Today's robots born and raised in simulation will be used in the real world to solve all manner of problems.
Simulation Research Is Propelling Progress Driven by researchers, recent simulation advances are rapidly improving the capabilities and flexibility of robotics systems, which is accelerating deployments.
University researchers, often working with NVIDIA Research and technical teams, are solving problems in simulation that have real-world impact. Their work is expanding the potential for commercialization of new robotics capabilities across numerous markets.
Among them, robots are learning to cut squishy materials such as beef and chicken, fasten nuts and bolts for automotive assembly, as well as maneuver with collision-free motion planning for warehouses and manipulate hands with new levels of dexterity.
Such research has commercial promise across trillion-dollar industries.
High-Fidelity, Physics-Based Simulation Breakthroughs The ability to model physics, displayed in high resolution, ushered in the start of many industrial advances.
Researched for decades, simulations based on physics offer commercial breakthroughs today.
NVIDIA PhysX, part of Omniverse core technology, delivers high-fidelity physics-based simulations, enablin
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Robots are moving goods in warehouses, packaging foods and helping assemble vehicles - when they're not flipping burgers or serving lattes.
