2021/07/25 by Bolun Dai, Dai, Bolun
Computer Science · Engineering · #Actuator #Artificial intelligence #Computer science #Control (management) #Control engineering #Control theory (sociology) #Degrees of freedom (physics and chemistry) #Engineering #FOS: Computer and information sciences #Inverted pendulum #Kinematic chain #Kinematics #Physics #Robot #Robot Manipulation and Learning #Robotic Locomotion and Control #Robotic Mechanisms and Dynamics #Robotics (cs.RO) #Simulation #cs.RO
paper · pdf · doi:10.48550/arxiv.2107.11836
arxiv created 2021/07/25 · openalex publication_date 2021/07/25 · arxiv updated 2021/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.