Yes, it is suitable for installation-free model inspection, teaching, and collaboration, but not for high-throughput training or strict real-time work in the same browser path because the README explicitly says WebAssembly is slower than native C.

• The README directs users to mujoco.org to explore MuJoCo on desktop or mobile with nothing to install.
• live.mujoco.org uses the WebAssembly version of MuJoCo Studio and supports inspecting and dragging in user-owned models.
• Interactive visualization in web browsers makes model sharing and quick validation practical.
• The README does not specify browser compatibility, per-model size limits, offline deployment, or performance boundaries on mobile devices.

Yes, MuJoCo is a good fit because the README provides Python bindings, an MJX/JAX workflow, and a differentiable-physics locomotion example; actual training throughput still depends on model features and accelerator support.

• The native Python bindings require Python 3.11 or newer and are installable from PyPI.
• The MJX tutorial demonstrates MuJoCo XLA, providing a path into JAX-based vectorization and accelerated execution.
• The differentiable-physics tutorial trains locomotion policies with analytical gradients derived from MuJoCo’s physics step.
• The README only states that WebAssembly is slower than native C; it does not provide performance data for a specific GPU, model size, or operator coverage.

Yes, MuJoCo is a suitable candidate for adding multithreaded rollouts to a Python training pipeline because the README provides a dedicated rollout tutorial and explicitly describes the module as multithreaded. Whether it reaches your throughput target still depends on the model and concurrency scale.

• The Python bindings are installable from PyPI, making direct integration with Python training code straightforward.
• Getting Started lists a rollout tutorial that demonstrates the multithreaded rollout module.
• The native runtime uses preallocated low-level data structures and is tuned for performance.
• The README does not specify a rollout thread limit, steps-per-second results for different models, or benchmarks with a particular reinforcement-learning framework, so final throughput cannot be inferred from the documentation.

It depends: the README explicitly provides an OpenSim-to-MJCF conversion path and official Python and C APIs, but whether the migration reproduces your biomechanics experiment depends on which model elements and parameters the converter can express accurately.

• The Converters section lists MyoConverter and states that it converts OpenSim models to MJCF.
• MuJoCo names biomechanics as a target area and supports articulated structures interacting with their environment.
• The C API and Python bindings provide official integration paths for programmable experiments and automation.
• The README does not document converter coverage, muscle-model mappings, error metrics, or validation results for a particular OpenSim model, so successful conversion alone does not establish biomechanical equivalence.

Yes, MuJoCo is suitable for a research-oriented or dynamics-driven Unity prototype because it explicitly covers articulated dynamics, collisions, contacts, and friction and provides C# bindings plus a Unity plug-in. The README does not promise a complete game-production toolchain.

• The README names robotics, biomechanics, graphics, and animation among its target areas.
• The core runtime includes joints, actuators, constraints, collisions, contacts, and friction.
• The official bindings list includes C# bindings and a Unity plug-in, reducing the need to wrap the C API yourself.
• Native desktop windows and browsers provide interactive visualization, but the README does not describe complete Unity coverage for rendering, asset management, or shipping workflows.

It depends: MuJoCo’s architecture fits this control-loop pattern well, and Linux AArch64 is an explicitly supported prebuilt target, but the README does not promise hard real-time behavior or a control frequency on your hardware.

• MuJoCo exposes a C API, and its runtime operates on low-level data structures preallocated by the built-in XML compiler, which is intended to maximize performance.
• Prebuilt releases support Linux x86-64 and AArch64, so your platform is within the documented release targets.
• Model compilation is separated from runtime simulation, allowing model loading to remain outside the control loop.
• The README does not specify hard real-time guarantees, maximum control rates, scheduling requirements, or benchmark results for a particular AArch64 chip.