Task-Space Control Interface for SoftBank Humanoid Robots and its Human-Robot Interaction Applications

Bolotnikova, Anastasia; Gergondet, Pierre; Tanguy, Arnaud; Courtois, Sébastien de; Kheddar, Abderrahmane

doi:10.1109/ieeeconf49454.2021.9382685

Cited by 9 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…motion In our proposed approach, motion control is facilitated through mc rtc, an open-source interface for robot simulation and control. This tool has been utilized in numerous humanoid loco-manipulation implementations [18,19,20]. It encompasses WBC and FSM, both of which are employed to execute the motion sequence of the vehicle ingress task.…”

Section: Motion Controlmentioning

confidence: 99%

Robust Humanoid Robot Vehicle Ingress with A Finite State Machine Integrated with Deep Reinforcement Learning

Wang,

Chen,

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Ingress task is crucial in a humanoid robot's attempt to drive a land vehicle and reach its destination fast. Previous work is inefficient in granting robots the ability to enter a vehicle from random starting position and orientation or withstand elasticity in vehicles, which are both hard to model. Deep Reinforcement Learning (DRL) could be introduced to address these issues. Previous applications of DRL in humanoid control tend to use consistent reward terms for the whole control process, which is not suitable for the ingress task with many distinctive states. This letter proposes a novel Finite State Machine control method integrated with Deep Reinforcement Learning for the humanoid ingress task. It collects the robot's status at the end of each state and immediately adjusts its next move. It has 97 percent ingress success rate with random initial displacement and vehicle elasticity in simulation.

show abstract

Section: Motion Controlmentioning

confidence: 99%

Robust Humanoid Robot Vehicle Ingress with A Finite State Machine Integrated with Deep Reinforcement Learning

Wang,

Chen,

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…the environment, the dense tracker can also be used to localize the robot [14]. This allows extending the closed-loop task-space mc_rtc controller [15], [16] to whole-body loco-manipulation tasks. We have performed experiments with two large objects, the hardest being inspired by a real industrial use-case we are investigating, and assess our approach with two different humanoid robots.…”

Section: Introductionmentioning

confidence: 99%

Humanoid Loco-Manipulations Using Combined Fast Dense 3D Tracking and SLAM With Wide-Angle Depth-Images

Chappellet

Murooka

Caron

et al. 2024

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

“…Humanoid robots are known for their high mobility and adaptability to diverse environments, driven by their complex degree of freedom (DOF) and dynamics [1]- [3]. While this abundance of DOF endows humanoid robots with the ability to perform various tasks [4]- [7], it also leads to challenges in controlling their intricate body design, power distribution, motion patterns, and movement algorithms [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

Humanoid Walking Control Using LQR and ANFIS

Auzan,

Lelono,

Dharmawan

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.

show abstract

Task-Space Control Interface for SoftBank Humanoid Robots and its Human-Robot Interaction Applications

Cited by 9 publications

References 11 publications

Robust Humanoid Robot Vehicle Ingress with A Finite State Machine Integrated with Deep Reinforcement Learning

Robust Humanoid Robot Vehicle Ingress with A Finite State Machine Integrated with Deep Reinforcement Learning

Humanoid Loco-Manipulations Using Combined Fast Dense 3D Tracking and SLAM With Wide-Angle Depth-Images

Humanoid Walking Control Using LQR and ANFIS

Contact Info

Product

Resources

About