基于强化学习的地形感知四足运动
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
AI总结：
提出一种基于强化学习的地形感知四足运动方法，通过DNN策略调整轨迹生成器参数并设计奖励函数，使机器人安全高效地穿越粗糙地形，仿真和实物实验验证了其有效性。
摘要：
在自然界中，四足动物通过感知能力发展出适应挑战性地形的能力，从而能够提前规划安全的身体和足部轨迹，实现安全且节能的运动。受此观察启发，我们提出了一种新方法，用于训练一个深度神经网络（DNN）策略，该策略将本体感觉和外感受状态与参数化轨迹生成器相结合，使四足机器人能够穿越粗糙地形。我们的关键思想是使用一个DNN策略，该策略能够修改轨迹生成器的参数（如足部高度和频率），以适应不同地形。为了鼓励机器人踩在安全区域并节省能耗，我们分别提出了足部地形奖励和抬足高度奖励。通过结合这些奖励，我们的方法能够学习到更安全、更高效的地形感知运动策略，使四足机器人能够灵活地向任意方向移动。为了评估我们方法的有效性，我们在具有挑战性的地形上进行了仿真实验，包括楼梯、踏脚石和杆子。仿真结果表明，我们的方法能够成功引导机器人向任意方向穿越这些艰难地形。此外，我们在一台真实四足机器人上验证了我们的方法，该机器人学会了穿越间隙超过25.5厘米的踏脚石。
英文摘要：
In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which leads to safe and energy-efficient locomotion. Inspired by this observation, we present a novel approach to train a Deep Neural Network (DNN) policy that integrates proprioceptive and exteroceptive states with a parameterized trajectory generator for quadruped robots to traverse rough terrains. Our key idea is to use a DNN policy that can modify the parameters of the trajectory generator, such as foot height and frequency, to adapt to different terrains. To encourage the robot to step on safe regions and save energy consumption, we propose foot terrain reward and lifting foot height reward, respectively. By incorporating these rewards, our method can learn a safer and more efficient terrain-aware locomotion policy that can move a quadruped robot flexibly in any direction. To evaluate the effectiveness of our approach, we conduct simulation experiments on challenging terrains, including stairs, stepping stones, and poles. The simulation results demonstrate that our approach can successfully direct the robot to traverse such tough terrains in any direction. Furthermore, we validate our method on a real legged robot, which learns to traverse stepping stones with gaps over 25.5cm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
