Ankle-hip-stepping stabilizer on tendon-driven humanoid Kengoro by integration of muscle-joint-work space controllers for knee-stretched humanoid balance

Asano, Yuki; Inaba, Masayuki; Nakashima, Shinsuke; Yanokura, Iori; Onitsuka, Moritaka; Kawaharazuka, Kento; Tsuzuki, K.; Koga, Yuya; Omura, Yusuke; Okada, Kei

doi:10.1109/humanoids43949.2019.9035008

Cited by 3 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…u opt seq ← u opt seq − γ∂L/∂u opt seq (10) where s pred seq is the predicted s [t+1:t+N step ] , h expand is the function of h expanded N step times, h loss is the loss function, and γ is the learning rate. Thus, the future s is predicted from the current sensor state s t by u opt seq , and u opt seq is optimized by using the backpropagation and gradient descent methods to minimize the loss function.…”

Section: E Balance Control Using Dpmpbmentioning

confidence: 99%

“…[kawaharazuka, ribayashi, miki, toshimitsu, t-suzuki, k-okada, inaba]@jsk.t.utokyo.ac.jp described so far deals with balancing. Although a simple balance control using PID has been implemented [10], it is difficult to say that its balance has improved because the convergence of zero moment point has not been evaluated, and the success rate of the step-out experiment is extremely low.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kawaharazuka

Ribayashi

Miki

et al. 2022

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

Self Cite

View full text Add to dashboard 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.

show abstract

Section: E Balance Control Using Dpmpbmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kawaharazuka

Ribayashi

Miki

et al. 2022

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, when impulsive or soft continuous perturbations are introduced, more rigid behaviors should counterbalance such disturbances. Different balancing strategies have been introduced in the state of the art, for instance, [2][3][4][5] among others. In [6], an ankle balancing strategy, where the Linear Inverted Pendulum Model (LIPM) augmented with virtual spring dampers, is used to generate a compliant response against external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Dynamic Balancing Controllers for Humanoid Robots

Pena

Humphreys

Hoffman³

et al. 2022

Robotics

View full text Add to dashboard Cite

This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass (CoM) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the CoM deviation. The first one uses the robot’s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a CoM reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the CoM deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure (CoP). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework.

show abstract

Research and Development of Tendon-driven Humanoids based on Human Mimetic Design Principles

Asano

2021

JRSJ

View full text Add to dashboard Cite

Ankle-hip-stepping stabilizer on tendon-driven humanoid Kengoro by integration of muscle-joint-work space controllers for knee-stretched humanoid balance

Cited by 3 publications

References 13 publications

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

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

Benchmarking Dynamic Balancing Controllers for Humanoid Robots

Research and Development of Tendon-driven Humanoids based on Human Mimetic Design Principles

Contact Info

Product

Resources

About