MuJoCoUni提升机器人学习中的批量物理仿真效率。
MuJoCoUni:Persistent Batched Runtime Primitives for MuJoCo
- 基于C++/pybind11构建批量环境池，支持多线程并行执行
- 实现高吞吐批量推理，保留原版MuJoCo的物理模型与约束语义
- 适合需要大规模仿真且需保持真实物理一致性的强化学习研究者
我们提出MuJoCoUni，一个面向在线机器人学习与批量物理评估的下游MuJoCo发行版。在上游mujoco.rollout提供的开环批量轨迹生成基础上，MuJoCoUni补充了状态化环境执行的运行时原语。目标工作负载需在保持上游CPU MuJoCo语义（包括模型、传感器、接触与约束）的前提下实现高吞吐并行执行。其核心对象BatchEnvPool是用C++/pybind11实现的执行器，管理每个环境的mjModel副本、每线程的mjData工作者及内部线程池。它提供仅返回终态的短步进、稀疏重置、重置生命周期域随机化、无需推进动力学的批量传感器前向计算，以及批量雅可比与高度场查询。实现限定于Python绑定层，而MuJoCo的求解器、接触模型、积分器及核心源码树仍保持上游语义。本文描述BatchEnvPool API、实现边界、与rollout的关系，以及随开源mujoco-uni包附带的验证与基准脚本，可通过pip install mujoco-uni安装。
原文摘要 · Abstract (English)
We present MuJoCoUni, a downstream MuJoCo distribution for online robot learning and batched physics evaluation. Alongside the open-loop batched trajectory generation already provided by upstream mujoco.rollout, MuJoCoUni supplies runtime primitives for stateful environment execution. The target workloads need high-throughput parallel execution while retaining upstream CPU MuJoCo semantics for models, sensors, contact, and constraints. Its core object, BatchEnvPool, is a C++/pybind11 executor that owns per-environment mjModel copies, per-thread mjData workers, and an internal thread pool. It provides final-state-only short stepping, sparse reset, reset-lifecycle domain randomization, batched sensor forward evaluation without advancing dynamics, and batched Jacobian and height-field queries. The implementation is confined to the Python binding layer; MuJoCo's solver, contact model, integrator, and core source tree retain upstream semantics. This report describes the BatchEnvPool API, implementation boundary, relationship to rollout, and the validation and benchmark scripts shipped with the open-source mujoco-uni package, which is installed with \texttt{pip install mujoco-uni}.
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