MuJoCo vs RaiSim: Multi-Contact Solver

MuJoCo excels at computational speed and differentiability because it relies on a convex approximation of contact dynamics. For example, in standard manipulation benchmarks, MuJoCo's constraint-based solver can achieve real-time factors exceeding 10,000x on a single CPU core for complex multi-joint systems, making it the de facto standard for massively parallel reinforcement learning (RL) training where throughput is paramount.

RaiSim takes a fundamentally different approach by employing a bisection-based, iterative solver that respects the full non-convex complementarity problem. This results in a critical trade-off: RaiSim achieves higher physical accuracy and energy conservation in stiff, multi-contact scenarios like quadruped locomotion on uneven terrain, but at a computational cost that typically limits simulation throughput to a few hundred environments per GPU compared to MuJoCo's thousands.

The key trade-off: If your priority is maximizing sample efficiency for RL training on tasks where approximate contact is sufficient (e.g., dexterous in-hand manipulation), choose MuJoCo. If you prioritize simulation fidelity and physically accurate force resolution for safety-critical contact-rich tasks (e.g., legged locomotion over rough terrain), choose RaiSim.

MuJoCo excels at producing smooth, stable, and differentiable contact dynamics because of its convex Gauss Principle solver. This mathematical formulation guarantees a unique solution for contact forces, making it exceptionally reliable for training reinforcement learning (RL) policies where gradient stability is paramount. For example, in dexterous in-hand manipulation tasks, MuJoCo's constraint-based approach prevents the jitter and penetration artifacts that can derail a training run, leading to higher policy success rates in sim-to-real transfers.

RaiSim takes a different approach by using a bisection-based iterative solver that prioritizes raw speed and hard contact accuracy over strict convexity. This results in a significant performance advantage for massively parallel simulations involving hundreds of environments. In legged locomotion benchmarks, RaiSim often achieves 2-3x faster real-time factors than MuJoCo on a single CPU thread, allowing researchers to iterate on gaits much faster. The trade-off is that its contact solutions are not strictly differentiable, which can introduce noise into gradient-based policy optimization.

The key trade-off: If your priority is differentiable, smooth contact dynamics for fine manipulation and stable RL gradients, choose MuJoCo. If you prioritize raw simulation throughput and hard contact fidelity for legged locomotion or massive parallel training, choose RaiSim. For teams needing both, a common pattern is to prototype in MuJoCo for policy quality and then port to RaiSim for scaled-up deployment training.