MuJoCoUni:Persistent Batched Runtime Primitives for MuJoCo
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}.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-centric execution path. This paradigm has …
Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.e. with a batch size of 1 and no replay buffer. …
Reinforcement LearningContinuous ControlMagicSim: A Unified Infrastructure for Executable Embodied Interaction
Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment. Existing pi…
DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed acyclic graph constrain…
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs
We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with macrobatching in which multiple minibatc…
CPUGPUgraph partitioning