What it does RoboHive is a collection of MuJoCo-based robot simulation environments exposed through the OpenAI-Gym API. It bundles dexterous hand manipulation, arm control, musculoskeletal models, and multi-task benchmarks into one installable package (pip install robohive). The pitch: plug any gym-compatible RL framework—Stable Baselines, RLlib, TorchRL—into any of the included suites without rewriting environment wrappers.

The interesting bit The musculoskeletal angle is genuinely unusual. The Myo-Suite and MyoDM-Suite model biological motor control rather than standard jointed robots, which opens a different research lane than yet another Franka arm benchmark. The project also originated the widely-used Adroit dexterous manipulation benchmarks.

Key highlights

• Five environment suites: Hand-Manipulation, Arm-Manipulation, Myo (musculoskeletal control), MyoDM (musculoskeletal dexterous manipulation), and MultiTask (includes RelayKitchen)
• TCDM Suite marked WIP; ROBEL Suite listed as “Coming soon”
• Supports visual encoders: R3M, RRL, VC (requires separate setup)
• One-command initialization via robohive_init
• Colab demo available

Caveats

• Two suites are incomplete or pending (TCDM WIP, ROBEL “Coming soon”)
• Visual encoder dependencies are not included in the base pip install
• Documentation lives in a GitHub wiki; depth unclear from README alone

Verdict Worth a look if you’re doing robot learning in MuJoCo and want consolidated, pre-wrapped benchmarks. Skip if you need real hardware integration now—ROBEL isn’t here yet, and the framework is simulation-only.