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Jan 22, 2025 | 
science.org | Ignacio Abadía |Grégoire Courtine |Auke Ijspeert |Eduardo Ros |Alice Bruel |Niceto R. Luque
Muscle cocontraction and cerebellar adaptation adjust robot accuracy and compliance, enabling a wide motor behavior spectrum.
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Dec 4, 2024 | 
nature.com | Hoang-Vu Phan |Auke Ijspeert
AbstractMost birds can navigate seamlessly between aerial and terrestrial environments. Whereas the forelimbs evolved into wings primarily for flight, the hindlimbs serve diverse functions such as walking, hopping and leaping, and jumping take-off for transitions into flight1. These capabilities have inspired engineers to aim for similar multimodality in aerial robots, expanding their range of applications across diverse environments.
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Jun 28, 2024 | 
arxiv.org | Guillaume Bellegarda |Auke Ijspeert
[Submitted on 28 Jun 2024] Title:Learning Human-Robot Handshaking Preferences for Quadruped Robots View a PDF of the paper titled Learning Human-Robot Handshaking Preferences for Quadruped Robots, by Alessandra Chappuis and 2 other authors View PDF HTML (experimental) Abstract:Quadruped robots are showing impressive abilities to navigate the real world. If they are to become more integrated into society, social trust in interactions with humans will become increasingly important.
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Apr 30, 2024 | 
thetechstreetnow.com | Guillaume Bellegarda |Milad Shafiee |Auke Ijspeert
26 minutes ago phys.org A four-legged robot trained with machine learning by EPFL researchers has learned to avoid falls by spontaneously switching between walking, trotting, and pronking—a milestone for roboticists as well as biologists interested in animal locomotion.
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Apr 27, 2024 | 
arxiv.org | Milad Shafiee |Peizhuo Li |Guillaume Bellegarda |Auke Ijspeert
[Submitted on 27 Apr 2024] Title:Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion View a PDF of the paper titled Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion, by Ge Sun and 5 other authors View PDF HTML (experimental) Abstract:Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the...
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Apr 8, 2024 | 
nature.com | Milad Shafiee |Guillaume Bellegarda |Auke Ijspeert
AbstractQuadruped animals are capable of seamless transitions between different gaits. While energy efficiency appears to be one of the reasons for changing gaits, other determinant factors likely play a role too, including terrain properties. In this article, we propose that viability, i.e., the avoidance of falls, represents an important criterion for gait transitions.
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Nov 6, 2023 | 
nature.com | Tomislav Milekovic |Eduardo Martin Moraud |Charlotte Moerman |Shiqi Sun |Matthew G. Perich |Robin Demesmaeker | +17 more
AbstractPeople with late-stage Parkinson’s disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking.
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Sep 26, 2023 | 
biorxiv.org | Jonathan Arreguit |Shravan Tata Ramalingasetty |Auke Ijspeert
AbstractThe study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to neuromechanical simulations for modeling, simulation, and analysis of animal locomotion and bio-inspired robotic systems.
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Aug 21, 2023 | 
nature.com | Wanming Yu |Auke Ijspeert |Christopher McGreavy |Eleftherios Triantafyllidis |Guillaume Bellegarda
AbstractRobot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering efforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance.
