Abstract: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 this http URL, 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}.
| Comments: | Technical report |
| Subjects: | Robotics (cs.RO) |
| MSC classes: | 68T40 |
| ACM classes: | I.2.9 |
| Cite as: | arXiv:2605.24922 [cs.RO] |
| (or arXiv:2605.24922v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24922 arXiv-issued DOI via DataCite (pending registration) |
From: Yufei Jia [view email]
[v1]
Sun, 24 May 2026 07:57:22 UTC (1,068 KB)
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