Google DeepMind has released a major upgrade to MuJoCo, its open-source physics engine for training and testing robotics systems through simulation. MuJoCo 3 introduces accelerated performance, scaled multi-threading, and more lifelike collision handling to expand possibilities for artificial agents learning complex motor skills.

Up to now, reinforcement learning using MuJoCo's realistic models required ample computational power over prolonged trials. MuJoCo 3's integration with Google Cloud TPUs and other hardware accelerators brings orders-of-magnitude speedup, enabling advanced experiments previously intractable.

Optimized scene partitioning now also allows single large environments to be divided across threads for responsive parallel execution. This technique brought 3x faster simulation for a scene with 22 humanoid agents, even without special hardware.

The update's new signed distance fields likewise promise more accurate, flexible collision detection between objects like gears or threaded fasteners. These advancements provide finer-grained physical feedback as virtual agents try manipulating a wider array of smooth and irregular objects.

According to DeepMind's robotics team lead, "the improvements in MuJoCo 3 empower new research into policies that require precise, dynamic manipulation - bringing us closer to agents that can learn complex behaviors like opening doors or assembling furniture."

Since DeepMind open-sourced MuJoCo in 2021, its uptake proved foundational algorithms initially needing weeks can become practical overnight. These latest optimizations aim to make the platform even more accessible to robotics developers needing to prototype intricate mechanical systems.

Whether scaling up indoor navigation maneuvers or prototyping industrial robots, MuJoCo 3's upgraded feature set lowers the barriers for both academic exploration and commercial applications. Pushing simulation fidelity, efficiency and extensibility will drive the next breakthroughs in real-world autonomous agents.