Biped locomotion - Improvement and adaptation

Teixeira, Carlos José Fortunas; Costa, Lino; Santos, Cristina P.

doi:10.1109/icarsc.2014.6849771

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The bio-inspired architectures presented in the previous chapter are capable of producing stable and robust locomotion in the Oncilla robot. In order to evaluate the gait performance of this robot, we implement a function that comprises three components, based on a previous team work (Teixeira et al, 2014). Thereby, we evaluate the robot's frontal displacement (disp), the gait's harmony (harm) and the robot's stability (stab).…”

Section: Gait Analysismentioning

confidence: 99%

Robotic locomotion combining Central Pattern Generators and reflexes

Ferreira

Santos

2015

2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG)

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All the work carried during the past year would not be possible without the help, understanding and support from many people. First and foremost, I would like to express my deepest gratitude to my adviser Prof. Cristina Santos, who allowed me the possibility to integrate the CAR (Control, Automation and Robotics) group of the ALGORITMI center. I am extremely grateful for her dedication, support and constant motivation, providing me an excellent atmosphere to complete my thesis. I am sincerely grateful for her excellent guidance and perseverance, encouraging me to tackle the different problems of this research. The help, constructive criticism and advice that I received were invaluable to the final quality of this work, making suggestions that contributed to overcoming the difficulties that emerged during this journey. I would like to thank Prof. Lino Costa for the opportunity to learn new topics related with fitness evaluation. I want to express my gratitude to Prof. Auke Ijspeert, who was always available to help with whatever was needed, provided insighful discussions about the research and presented suggestions that helped me develop this accomplishment. I am especially thankful to everybody in the lab, for all the friendship and companionship that provided me with an excellent daily environment. A special thanks to João Macedo, who was always available to help me solve computational issues. I express my warm thank you to my girlfriend Bárbara for her support, dedication and patience during the last year. She assisted me when I was writing this thesis, giving me some insight to stay on track and complete this work successfully. Finally, I would like to thank my family, my parents and my brother, for their constant support, love and encouragement.

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Section: Gait Analysismentioning

confidence: 99%

Robotic locomotion combining Central Pattern Generators and reflexes

Ferreira

Santos

2015

2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG)

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“…However, the optimal adjustments of robot motions or step parameters that compensate for dynamic disturbances should be determined beforehand. To cope with these prerequisites, Reinforcement Learning (RL) was introduced into the Dynamic Movement Primitive (DMP) algorithm [29]. Following this idea, the task-space DMP [30] and the joint-space DMP [31] were employed for push recovery.…”

Section: Introductionmentioning

confidence: 99%

Robust Gait Synthesis Combining Constrained Optimization and Imitation Learning

Ding

Xiao

Tsagarakis

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Despite plenty of motion planning strategies have been proposed for bipedal locomotion, enhancing the walking robustness in real-world environments is still an open question. This paper focuses on robust body and leg trajectories synthesis through integrating constrained optimization with imitation learning. Specifically, we first propose a Quadratically Constrained Quadratic Programming (QCQP) algorithm to make use of the ankle strategy and stepping strategy. Based on the Linear Inverted Pendulum (LIP) model, body motion can be determined by the modulated Center of Pressure (CoP) position and step parameters (including step location and step duration). After that, we exploit an imitation learning approach Kernelized Movement Primitives (KMP) to plan robot leg motions, which allows for adapting the learned motion patterns to new situations (e.g., passing through various desired points) in a straightforward manner. Several LIP simulations and whole-body dynamic simulations demonstrate that higher walking robustness can be achieved using our framework.

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A sensory-driven controller for quadruped locomotion

Ferreira

Santos

2017

Biol Cybern

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Locomotion of quadruped robots has not yet achieved the harmony, flexibility, efficiency and robustness of its biological counterparts. Biological research showed that spinal reflexes are crucial for a successful locomotion in the most varied terrains. In this context, the development of bio-inspired controllers seems to be a good way to move toward an efficient and robust robotic locomotion, by mimicking their biological counterparts. This contribution presents a sensory-driven controller designed for the simulated Oncilla quadruped robot. In the proposed reflex controller, movement is generated through the robot's interactions with the environment, and therefore, the controller is solely dependent on sensory information. The results show that the reflex controller is capable of producing stable quadruped locomotion with a regular stepping pattern. Furthermore, it is capable of dealing with slopes without changing the parameters and with small obstacles, overcoming them successfully. Finally, system robustness was verified by adding noise to sensors and actuators and also delays.

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Biped locomotion - Improvement and adaptation

Cited by 4 publications

References 23 publications

Robotic locomotion combining Central Pattern Generators and reflexes

Robotic locomotion combining Central Pattern Generators and reflexes

Robust Gait Synthesis Combining Constrained Optimization and Imitation Learning

A sensory-driven controller for quadruped locomotion

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