RealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning

Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular 'Ant' benchmark used in reinforcement learning. RealAnt costs only $410 in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the TD3 algorithm can learn to walk the RealAnt from less than 45 minutes of experience. We also provide simulator versions of the robot (with the same dimensions, state-action spaces, and delayed noisy observations) in the MuJoCo and PyBullet simulators. We open-source hardware designs, supporting software, and baseline results for ease of reproducibility.