Model-based control usually relies on an accurate model, which is often obtained from CAD and actuator models. The more accurate the model the better the control performance. However, in bipedal robots that demonstrate high agility actions, such as running and hopping, the robot hardware will suffer from impacts with the environment and deform in vulnerable parts, which invalidates the predefined model. Thus, it is desired to have an adaptable kinematic structure that takes deformation into consideration. To account for this we propose an approach that models all of the robotic joints as 6-DOF joints and develop an algorithm that can identify the kinematic structure from motion capture data. We evaluate the algorithm's performance both in simulation - a three link pendulum, and on a bipedal robot - ATRIAS. In the simulated case the algorithm produces a result that has a 3.6% error compared to the ground truth, and on the real life bipedal robot the algorithm's result confirms our prior assumption where the joint deforms on out-of-plane degrees of freedom. In addition our algorithm is able to predict torques and forces using the reconstructed joint mode.

翻译：基于模型的控制通常依赖于精确的模型, 该模型通常来自 CAD 和 驱动器模型。 模型越准确, 控制性能就越好。 但是, 在显示高度敏捷动作的双翼机器人中, 如运行和跳跃, 机器人硬件将受到环境的影响, 脆弱部分的变形使预设模型失效。 因此, 它希望有一个适应性运动结构, 将变形考虑在内。 为此, 我们提出了一个方法, 模型中所有机器人接合为 6- DOF 联合, 并开发一种算法, 能够识别运动捕获数据的运动结构。 我们在模拟中评估算法的性能―― 3个链接, 和双向机器人- ATRAAS 。 在模拟中, 算法产生的结果与地面真相相比有3. 6% 错误的结果, 在真实生活中, 双向机器人的算法结果证实了我们先前的假设, 即所有机器人联机外自由度变形的计算方法。 此外, 我们的算法能够预测使用联合模式对托克和力进行重组。