ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders
Reinforcement learning for legged locomotion has matured into a stack of multi-component reward functions and physics-engine benchmarks whose morphologies are uniformly derived from real commercial hardware. Game NPCs, however, are bound by stylistic constraints absent from sim-to-real robotics and routinely take the form of creatures with no real-robot counterpart. We introduce ARC-RL, a suite of four MuJoCo continuous-control environments featuring robotic morphologies inspired by the bestiary of ARC Raiders: the 18-DoF tall hexapod Queen, the 12-DoF armoured hexapod Bastion, the 18-DoF compact hexapod Tick, and the 12-DoF quadruped Leaper. All four robots share a unified observation te...
| Authors | Carlo Romeo, Andrew D. Bagdanov |
|---|---|
| Primary category | cs.RO |
| Published | 19 May 2026 |
| Last updated at source | 20 May 2026 |
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