ROBOTICS & PHYSICAL AI

Bimanual stationery manipulation

Bimanual stationery manipulation is an OpenEnv-compatible reinforcement learning environment in our Robotics & Physical AI catalog. An agent acts through the tools below, and a deterministic verifier scores every episode, so the reward can be used for GRPO or other RLVR post-training.

Task and verification

Coordinate two Cartesian grippers in automatic MuJoCo simulation: uncap a marker, extract an eraser or pad, pour clip proxies, and hand over a ruler. Server-owned physical state, bilateral contacts, force limits, and stable task completion determine reward.

Tools and runtime

MuJoCo 3.3.7 rigid-body simulation inspired by StationeryBench; text/tool planning, not an official benchmark score or hardware qualification

OPENENV / MUJOCO

Related Robotics & Physical AI gyms

• Autonomous driving (CARLA)
• ManiSkill articulation & contact
• LeRobot imitation & policy learning
• PX4 drone aerial navigation
• Dynamic balance & locomotion (Unitree G1 / Go1)
• Sim-to-real robotic calibration
• Real robot arm manipulation (Viam)
• Mobile robot indoor navigation (Gazebo)
• Multi-robot swarm coordination (Robotarium)
• MuJoCo batched CPU rollouts (MjBatch)

See how these environments are run privately on the custom RL environments page, and how their rewards are made hard to game in our verifier design guide.

Plan a training run

Check the environment’s availability, compatible models and compute in Studio. A catalog entry does not guarantee that hosted training is enabled for your workspace.