Hexapod robots are potentially suitable for carrying out tasks in cluttered environments since they are stable, compact, and light weight. They also have multi-joint legs and variable height bodies that make them good candidates for tasks such as stairs climbing and squeezing under objects in a typical home environment or an attic. Expanding on our previous work on joist climbing in attics, we train a legged hexapod equipped with a depth camera and visual inertial odometry (VIO) to perform three tasks: climbing stairs, avoiding obstacles, and squeezing under obstacles such as a table. Our policies are trained with simulation data only and can be deployed on lowcost hardware not requiring real-time joint state feedback. We train our model in a teacher-student model with 2 phases: In phase 1, we use reinforcement learning with access to privileged information such as height maps and joint feedback. In phase 2, we use supervised learning to distill the model into one with access to only onboard observations, consisting of egocentric depth images and robot pose captured by a tracking VIO camera. By manipulating available privileged information, constructing simulation terrains, and refining reward functions during phase 1 training, we are able to train the robots with skills that are robust in non-ideal physical environments. We demonstrate successful sim-to-real transfer and achieve high success rates across all three tasks in physical experiments.

翻译：六足机器人因其稳定性高、结构紧凑且重量轻的特点，在复杂环境中执行任务具有潜在优势。其多关节腿部结构与可变高度机身设计，使其特别适用于家庭环境或阁楼等场景中的爬楼梯、钻越障碍物等任务。基于先前在阁楼横梁攀爬研究的基础，本研究训练搭载深度相机与视觉惯性里程计（VIO）的六足机器人完成三项任务：爬楼梯、避障以及钻越桌椅类障碍物。所有策略仅通过仿真数据训练完成，可部署于无需实时关节状态反馈的低成本硬件平台。训练采用两阶段师生模型：第一阶段利用强化学习，模型可获取高度图与关节反馈等特权信息；第二阶段通过监督学习将模型蒸馏为仅依赖车载观测数据的版本，包括由跟踪式VIO相机采集的自我中心深度图像与机器人位姿。通过调控特权信息、构建仿真地形及优化第一阶段训练的奖励函数，我们成功训练出能在非理想物理环境中保持鲁棒性的机器人技能。实验证实了仿真到现实的有效迁移，在实体测试中三项任务均达到较高成功率。