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Vsim develops GPU-optimized virtual environment for rapid robot training
Key points
- Vsim's software trains robots in minutes, not days.
- The system is optimized for GPUs, unlike older algorithms.
- Robots can run 20,000 simulations per second while moving.
- Founders previously worked on Nvidia's Isaac Sim.
Rapid Robot Training
Vsim, a British startup, has developed a virtual simulation environment that significantly reduces the time required to train robots. Their robot, Freddo, learned to walk, recognize, and grasp a bottle in minutes, a task that developers claim takes days with rival systems. This speed is attributed to Vsim's software, which allows tasks to be performed millions of times in simulation before being uploaded to hardware.
GPU Optimization
Founders Michelle Lu and Kier Storey, who previously worked on Nvidia's Isaac Sim, established Vsim in 2022 to build their own training system. They optimized their software to exploit the power of graphics processing units (GPUs), which are crucial for AI computations. Storey noted that traditional robotic simulation algorithms from the 1970s and 1980s are not well-suited for GPUs, leading to slower performance.
Real-time Simulation Capabilities
Vsim's software is efficient enough to run directly on a robot's hardware. This capability allows a robot like Freddo to perform tens of thousands of simulations while in motion. Storey explained that the robot can look approximately one second ahead, considering 20,000 different potential scenarios, which is vital for navigation in unstructured environments.
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Reporting from
British startup Vsim created a virtual simulation environment that trains robots in minutes, a process that typically takes days for rival systems. The software optimizes for GPUs, enabling robots to run thousands of simulations in real-time while operating.