Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot
This article presents the mechanical design of Otto, a lightweight 8-degrees-of-freedom (8-DoF) quadrupedal robot employing series elastic actuators, and a training framework for learning locomotion control policies in simulation using reinforcement learning (RL). Otto's design differs from typ...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10988638/ |
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| author | Antonello Scaldaferri Simone Tolomei Francesco Iotti Paolo Gambino Michele Pierallini Franco Angelini Manolo Garabini |
| author_facet | Antonello Scaldaferri Simone Tolomei Francesco Iotti Paolo Gambino Michele Pierallini Franco Angelini Manolo Garabini |
| author_sort | Antonello Scaldaferri |
| collection | DOAJ |
| description | This article presents the mechanical design of Otto, a lightweight 8-degrees-of-freedom (8-DoF) quadrupedal robot employing series elastic actuators, and a training framework for learning locomotion control policies in simulation using reinforcement learning (RL). Otto's design differs from typical 12-DoF quadrupeds by lacking hip adduction–abduction DoF. This reduces the robot's cost and weight and increases complexity for tasks involving base rotation and angular twist following. The elastic elements at the joints improve compliance, energy efficiency, safety, and stability, increase robustness, and reduce damage to robot hardware components. Our locomotion control approach leverages RL to optimize policies in simulation, allowing stable and efficient movement despite mechanical constraints, i.e., an 8-DoF quadrupedal robot. Through extensive simulation training, leveraging highly parallel Graphics Processing Unit (GPU)-accelerated robotic simulators, we ensure the policy is well-suited for deployment in real-world scenarios, where accurate motion control is critical for performance. The trained policy is then transferred to the physical robot platform. We demonstrate its effectiveness in various tasks and real-life scenarios with varying payloads and terrains, and compare it with a state-of-the-art model-based method. The results show that Otto, equipped with our RL-based locomotion control, achieves robust performance, compensating for the reality gap and managing the reduced DoF available in Otto. |
| format | Article |
| id | doaj-art-230e4898006b4e7bbf13806ee1d2b714 |
| institution | DOAJ |
| issn | 2644-1284 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-230e4898006b4e7bbf13806ee1d2b7142025-08-20T03:06:05ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842025-01-01682083910.1109/OJIES.2025.356711210988638Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal RobotAntonello Scaldaferri0https://orcid.org/0000-0002-7146-1635Simone Tolomei1https://orcid.org/0009-0006-7928-3518Francesco Iotti2https://orcid.org/0009-0000-9257-6231Paolo Gambino3https://orcid.org/0009-0000-4204-4413Michele Pierallini4https://orcid.org/0000-0003-0547-2747Franco Angelini5https://orcid.org/0000-0003-2559-9569Manolo Garabini6https://orcid.org/0000-0002-5873-3173Centro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyCentro di Ricerca “Enrico Piaggio” and Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ItalyThis article presents the mechanical design of Otto, a lightweight 8-degrees-of-freedom (8-DoF) quadrupedal robot employing series elastic actuators, and a training framework for learning locomotion control policies in simulation using reinforcement learning (RL). Otto's design differs from typical 12-DoF quadrupeds by lacking hip adduction–abduction DoF. This reduces the robot's cost and weight and increases complexity for tasks involving base rotation and angular twist following. The elastic elements at the joints improve compliance, energy efficiency, safety, and stability, increase robustness, and reduce damage to robot hardware components. Our locomotion control approach leverages RL to optimize policies in simulation, allowing stable and efficient movement despite mechanical constraints, i.e., an 8-DoF quadrupedal robot. Through extensive simulation training, leveraging highly parallel Graphics Processing Unit (GPU)-accelerated robotic simulators, we ensure the policy is well-suited for deployment in real-world scenarios, where accurate motion control is critical for performance. The trained policy is then transferred to the physical robot platform. We demonstrate its effectiveness in various tasks and real-life scenarios with varying payloads and terrains, and compare it with a state-of-the-art model-based method. The results show that Otto, equipped with our RL-based locomotion control, achieves robust performance, compensating for the reality gap and managing the reduced DoF available in Otto.https://ieeexplore.ieee.org/document/10988638/Articulated soft robotslocomotion controlquadrupedal robotsreinforcement learning (RL)sim2real transfer |
| spellingShingle | Antonello Scaldaferri Simone Tolomei Francesco Iotti Paolo Gambino Michele Pierallini Franco Angelini Manolo Garabini Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot IEEE Open Journal of the Industrial Electronics Society Articulated soft robots locomotion control quadrupedal robots reinforcement learning (RL) sim2real transfer |
| title | Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot |
| title_full | Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot |
| title_fullStr | Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot |
| title_full_unstemmed | Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot |
| title_short | Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot |
| title_sort | otto x2014 design and control of an 8 dof sea driven quadrupedal robot |
| topic | Articulated soft robots locomotion control quadrupedal robots reinforcement learning (RL) sim2real transfer |
| url | https://ieeexplore.ieee.org/document/10988638/ |
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