Online Learning of Joint-Muscle Mapping Using Vision in Tendon-Driven Musculoskeletal Humanoids

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)

Miki

Bando

et al. 2022

Self Cite

Various musculoskeletal humanoids have been developed so far. While these humanoids have the advantage of their flexible and redundant bodies that mimic the human body, they are still far from being applied to real-world tasks. One of the reasons for this is the difficulty of bipedal walking in a flexible body. Thus, we developed a musculoskeletal wheeled robot, Musashi-W, by combining a wheeled base and musculoskeletal upper limbs for real-world applications. Also, we constructed its software system by combining static and dynamic body schema learning, reflex control, and visual recognition. We show that the hardware and software of Musashi-W can make the most of the advantages of the musculoskeletal upper limbs, through several tasks of cleaning by human teaching, carrying a heavy object considering muscle addition, and setting a table through dynamic cloth manipulation with variable stiffness.

Section: A Static Body Schema Learningmentioning

confidence: 99%

Hardware Design and Learning-Based Software Architecture of Musculoskeletal Wheeled Robot Musashi-W for Real-World Applications

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)

Miki

Bando

et al. 2022

Self Cite

“…Since the muscle wires are made of Dyneema ® , an abrasion resistant synthetic fiber, and are surrounded by a soft foam cover, their elasticity provides the flexibility of the body. By learning the relationship between muscle length, muscle tension, and joint angle, it is possible to control the joint angle [20]- [22]. However, due to the effects of friction and hysteresis, it is not always possible to control the joint angle accurately enough.…”

Section: A Overview Of Musashiolegsmentioning

confidence: 99%

Realization of Seated Walk by a Musculoskeletal Humanoid with Buttock-Contact Sensors From Human Constrained Teaching

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Okada

Inaba

2022

Self Cite

In this study, seated walk, a movement of walking while sitting on a chair with casters, is realized on a musculoskeletal humanoid from human teaching. The body is balanced by using buttock-contact sensors implemented on the planar interskeletal structure of the human mimetic musculoskeletal robot. Also, we develop a constrained teaching method in which one-dimensional control command, its transition, and a transition condition are described for each state in advance, and a threshold value for each transition condition such as joint angles and foot contact sensor values is determined based on human teaching. Complex behaviors can be easily generated from simple inputs. In the musculoskeletal humanoid MusashiOLegs, forward, backward, and rotational movements of seated walk are realized.

“…In [6], for a relatively simple system with one or two joints, the relationship among joints, muscles, and tasks is trained, and a robot is controlled using the trained neural network. In [7], [8], for a more complex system, the relationship among joint angle, muscle tension, and muscle length is modelized by a neural network, which is trained and applied mainly to upper body control and state estimation. [9] has succeeded in recognizing grasped objects and stabilizing tool grasping by learning the dynamics of a musculoskeletal hand.…”

Section: Introductionmentioning

confidence: 99%

Learning of Balance Controller Considering Changes in Body State for Musculoskeletal Humanoids

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Ribayashi

Miki

et al. 2022

Self Cite

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.