Bipedal locomotion is a key challenge in robotics, particularly for robots like Bolt, which have a point-foot design. This study explores the control of such underactuated robots using constrained reinforcement learning, addressing their inherent instability, lack of arms, and limited foot actuation. We present a methodology that leverages Constraints-as-Terminations and domain randomization techniques to enable sim-to-real transfer. Through a series of qualitative and quantitative experiments, we evaluate our approach in terms of balance maintenance, velocity control, and responses to slip and push disturbances. Additionally, we analyze autonomy through metrics like the cost of transport and ground reaction force. Our method advances robust control strategies for point-foot bipedal robots, offering insights into broader locomotion.

翻译：双足步行是机器人学中的一个关键挑战，尤其对于像Bolt这样具有点足设计的机器人。本研究探索了使用约束强化学习来控制此类欠驱动机器人，以解决其固有的不稳定性、缺乏手臂以及足部驱动有限的问题。我们提出了一种方法，利用"约束即终止"和领域随机化技术来实现从仿真到现实的迁移。通过一系列定性和定量实验，我们从平衡维持、速度控制以及对滑移和推力扰动的响应等方面评估了我们的方法。此外，我们还通过运输成本和地面反作用力等指标分析了自主性。我们的方法推进了点足双足机器人的鲁棒控制策略，为更广泛的步行研究提供了见解。