让机器人走路更省电，通过智能判断踩脚时机和优化步态参数。
Energy-Efficient Motion Planner for Legged Robots
- 基于髋部下方虚拟落脚点集判断何时迈步，实现动态节能控制。
- 在低中速范围内能耗降低50.4%，比强化学习基线更节能且更鲁棒。
- 适用于复杂地形与扰动场景，已在Unitree A1硬件上验证。
我们提出一种在线运动规划方法，旨在提升腿式机器人的能量效率。核心思路是利用基于机器人躯干位置的虚拟落脚点集，判断脚步执行时机：当脚超出该落脚点集范围时即执行迈步。此外，我们设计了一套参数优化框架，综合考虑能效与鲁棒性，根据行走速度动态调整落脚点集形状、步高和摆动时间等参数。实验表明，该规划器生成的轨迹具有较低的运输成本（CoT）和较高鲁棒性，优于无模型强化学习（RL）及基于生物犬类运动先验的动作模仿方法。在低至中等速度范围内，相较于表现最佳的基线，能耗降低50.4%。最后，我们在仿真和Unitree A1机器人硬件上验证了其在光滑地面、步态切换及外部扰动下的适应能力。
原文摘要 · Abstract (English)
We propose an online motion planner for legged robot locomotion with the primary objective of achieving energy efficiency. The conceptual idea is to leverage a placement set of footstep positions based on the robot's body position to determine when and how to execute steps. In particular, the proposed planner uses virtual placement sets beneath the hip joints of the legs and executes a step when the foot is outside of such placement set. Furthermore, we propose a parameter design framework that considers both energy-efficiency and robustness measures to optimize the gait by changing the shape of the placement set along with other parameters, such as step height and swing time, as a function of walking speed. We show that the planner produces trajectories that have a low Cost of Transport (CoT) and high robustness measure, and evaluate our approach against model-free Reinforcement Learning (RL) and motion imitation using biological dog motion priors as the reference. Overall, within low to medium velocity range, we show a 50.4% improvement in CoT and improved robustness over model-free RL, our best performing baseline. Finally, we show ability to handle slippery surfaces, gait transitions, and disturbances in simulation and hardware with the Unitree A1 robot.
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