Learning of Balance Controller Considering Changes in Body State for Musculoskeletal Humanoids
arXiv:2405.11803 · doi:10.1109/IROS47612.2022.9981051
Abstract
The musculoskeletal humanoid is difficult to modelize due to the flexibility and redundancy of its body, whose state can change over time, and so balance control of its legs is challenging. There are some cases where ordinary PID controls may cause instability. In this study, to solve these problems, we propose a method of learning a correlation model among the joint angle, muscle tension, and muscle length of the ankle and the zero moment point to perform balance control. In addition, information on the changing body state is embedded in the model using parametric bias, and the model estimates and adapts to the current body state by learning this information online. This makes it possible to adapt to changes in upper body posture that are not directly taken into account in the model, since it is difficult to learn the complete dynamics of the whole body considering the amount of data and computation. The model can also adapt to changes in body state, such as the change in footwear and change in the joint origin due to recalibration. The effectiveness of this method is verified by a simulation and by using an actual musculoskeletal humanoid, Musashi.
Accepted at IROS2022
References in corpus (10)
- Learning agile and dynamic motor skills for legged robots
- Learning robust perceptive locomotion for quadrupedal robots in the wild
- Solving Rubik's Cube with a Robot Hand
- Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems
- Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures
- Online Learning of Joint-Muscle Mapping Using Vision in Tendon-driven Musculoskeletal Humanoids
- Musculoskeletal AutoEncoder: A Unified Online Acquisition Method of Intersensory Networks for State Estimation, Control, and Simulation of Musculoskeletal Humanoids
- Object Recognition, Dynamic Contact Simulation, Detection, and Control of the Flexible Musculoskeletal Hand Using a Recurrent Neural Network with Parametric Bias
- Dynamic Cloth Manipulation Considering Variable Stiffness and Material Change Using Deep Predictive Model with Parametric Bias
- Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State