92 task gyms an agent can be trained against.

A gym gives the model real tools and a strict test: pass, get a reward. Fail, try again. Every gym below is an OpenEnv environment across 10 engineering verticals.

One run, one sealed boxgym run #48113 · isolated process/dev/shmdisk in RAMsimulatorrobot · chip+ verifier30 s caprun ends → everything above is wipedno disk writes · next run starts cleanwhat leaves the boxtrajectory log + scoreyour datanever leavesthe host
Every attempt runs in its own process with an in-memory filesystem and a hard timer. When it ends, the state is gone; only the trajectory and the verifier's score are kept for training.
01
ROBOTICS & PHYSICAL AI

Autonomous driving (CARLA)

CARLA 0.9.15 + ROS 2 + sensor fusion + rule verifiers
#autonomous-vehicles#carla#ros2

Navigate photorealistic multi-lane urban and highway environments in CARLA. Closed-loop sensor streams, trajectory tracking, collision avoidance, and traffic rule conformance gate reward.

OPENENV / CARLAView gym
02
ROBOTICS & PHYSICAL AI

ManiSkill articulation & contact

SAPIEN + ManiSkill 3 + articulated rigid-body physics
#manipulation#physics#contact

Manipulate articulated objects (doors, drawers, faucets, gears) using contact-rich physics in ManiSkill. Sensor feedback, joint torque limits, and multi-stage goal states determine success.

OPENENV / MANISKILLView gym
03
ROBOTICS & PHYSICAL AI

LeRobot imitation & policy learning

Hugging Face LeRobot + MetaWorld + Franka/UR5 kinematics
#imitation-learning#robotics#metaworld

Train dexterous robotic manipulation policies across MetaWorld and LeRobot benchmark tasks. Multi-camera observations, force feedback, and terminal spatial tolerances gate the reward.

OPENENV / LEROBOTView gym
04
ROBOTICS & PHYSICAL AI

PX4 drone aerial navigation

PX4 Autopilot + jMAVsim/Gazebo + 3D spatial dynamics
#uav#px4#aerospace

Execute 3D waypoint navigation, wind disturbance rejection, and agile obstacle avoidance for quadcopters. MAVLink telemetry, flight corridor boundaries, and soft landing criteria gate reward.

OPENENV / PX4View gym
05
ROBOTICS & PHYSICAL AI

Dynamic balance & locomotion (Unitree G1 / Go1)

MuJoCo 3.3 + torque-limited joint controllers + contact physics
#robotics#mujoco#humanoid-g1

Stabilize dynamic bipedal and quadrupedal gaits under lateral push disturbances and slippery terrain in MuJoCo. Joint torque limits, ground reaction forces, and zero-fall recovery gate reward.

OPENENV / MUJOCOView gym
06
ROBOTICS & PHYSICAL AI

Sim-to-real robotic calibration

MuJoCo + Genesis + recorded hardware telemetry verification
#sim-to-real#telemetry#calibration

Tune physical simulator parameters so replayed robot trajectories match recorded hardware telemetry within strict tolerance, compensating for motor latency and backlash.

OPENENV / SIM-TO-REALView gym
07
ROBOTICS & PHYSICAL AI

Real robot arm manipulation (Viam)

Viam robotics API + arm kinematics + camera depth verifiers
#hardware-api#viam#robot-arm

Control physical robotic manipulators over distributed Viam APIs. End-effector positioning, workspace bounding boxes, collision avoidance, and gripper force sensing gate execution.

OPENENV / VIAMView gym
08
ROBOTICS & PHYSICAL AI

Mobile robot indoor navigation (Gazebo)

Gazebo + ROS 2 Navigation2 (Nav2) + SLAM mapping
#amr#gazebo#ros2

Drive autonomous mobile robots (AMR) through dynamic warehouse layouts. LiDAR point clouds, visual odometry, costmap planning, and zero-collision obstacle clearance determine reward.

OPENENV / GAZEBOView gym
09
ROBOTICS & PHYSICAL AI

Multi-robot swarm coordination (Robotarium)

Robotarium + Control Barrier Functions + formation metrics
#swarms#cbf#coordination

Coordinate decentralized swarms of autonomous agents moving toward shared spatial formations. Control barrier functions (CBF) and inter-agent collision boundaries gate qualification.

OPENENV / ROBOTARIUMView gym
10
ROBOTICS & PHYSICAL AI

Bimanual stationery manipulation

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

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.

OPENENV / MUJOCOView gym
11
ROBOTICS & PHYSICAL AI

MuJoCo batched CPU rollouts (MjBatch)

Physical state, rotation, flight, stable landing, contact and work gates verify every replica. G1 uses a bounded reference task at nominal friction 0.6. Simulation qualification; model training speed and hardware performance are unmeasured.
#mujoco#mjbatch#batched-rollouts

Run native MuJoCo 3.11.0 / MjBatch 0.1.0 CPU batches with per-replica friction and actuator controls. Evaluate Go1 velocity tracking with a released PPO actor, G1 backflip MPC, arm friction identification from encoder recordings, and throwing-arm co-design.

NATIVE MUJOCO / MJBATCHView gym