Time-variable, event-based walking control for biped robots

Wahrmann, Daniel; Wu, Yizhe; Sygulla, Felix; Hildebrandt, Arne-Christoph; Wittmann, Robert; Seiwald, Philipp; Rixen, Daniel J.

doi:10.1177/1729881418768918

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Parameter adjustments take place upon each impact (swing leg touching the ground, when trajectories cross the Poincare section) based on ground reaction forces [56]. Event-based controllers have been extensively analyzed for bipedal locomotion [19,54,55] and have been implemented in simulation and practice [53,18,45,49]. Such an example is work done on ATRIAS 2.1 showing the use of symmetry to simplify and improve design for event-based controllers [19].…”

Section: Event-based Controlmentioning

confidence: 99%

Legged walking on inclined surfaces

Wang

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In this thesis, I need to express my gratitude to my advisor, Prof. Alireza Ramezani, for his persistent guidance throughout my entire master's journey. I would like to extend my appreciation to my thesis committee member Prof. Leeser and Prof. Shafai for their detailed suggestions on the thesis and attending my defense. And I need to thank Mingxi Jia for his support over the years. His enthusiasm for research and optimism about life have continued motivating me. I would also like to thank all of my lab mates, including Kaushik, Shreyansh, Adarsh, Shoghair, Bibek, Xuejian, Yizhe, and Xintao, for their help with hardware, experiments, control design, simulation, and consistent encouragement.

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Section: Event-based Controlmentioning

confidence: 99%

Legged walking on inclined surfaces

Wang

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Legged Robots, Design of

Hong¹,

Ahn²,

Hooks³

et al. 2020

Encyclopedia of Robotics

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Humanoid Robot Locomotion Control based on Perceptive Model

Wang

Huang

et al. 2019

2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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In contrast to quadruped robots that can navigate diverse terrains using a "blind" policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of freedom and inherently unstable morphology. However, incorporating perceptual signals often introduces additional disturbances to the system, potentially reducing its robustness, generalizability, and efficiency. This paper presents the Perceptive Internal Model (PIM), which relies on onboard, continuously updated elevation maps centered around the robot to perceive its surroundings. We train the policy using groundtruth obstacle heights surrounding the robot in simulation, optimizing it based on the Hybrid Internal Model (HIM), and perform inference with heights sampled from the constructed elevation map. Unlike previous methods that directly encode depth maps or raw point clouds, our approach allows the robot to perceive the terrain beneath its feet clearly and is less affected by camera movement or noise. Furthermore, since depth map rendering is not required in simulation, our method introduces minimal additional computational costs and can train the policy in 3 hours on an RTX 4090 GPU. We verify the effectiveness of our method across various humanoid robots, various indoor and outdoor terrains, stairs, and various sensor configurations. Our method can enable a humanoid robot to continuously climb stairs and has the potential to serve as a foundational algorithm for the development of future humanoid control methods.

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Time-variable, event-based walking control for biped robots

Cited by 6 publications

References 34 publications

Legged walking on inclined surfaces

Legged walking on inclined surfaces

Legged Robots, Design of

Humanoid Robot Locomotion Control based on Perceptive Model

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