activity
20192025
most citedDeepTransition: Viability Leads to the Emergence of Gait Transitions in Learning Anticipatory Quadrupedal Locomotion Skills

6 citations · 11 across the 14 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.RO2023

ExoRecovery: Push Recovery with a Lower-Limb Exoskeleton based on Stepping Strategy

Zeynep Özge Orhan, Milad Shafiee, Vincent Juillard +3

Balance loss is a significant challenge in lower-limb exoskeleton applications, as it can lead to potential falls, thereby impacting user safety and confidence. We introduce a cont…

cs.RO2023★ 2 cited

ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots

Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert

Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated f…

cs.RO2023★ 1 cited

Identifying Important Sensory Feedback for Learning Locomotion Skills

Wanming Yu, Chuanyu Yang, Christopher McGreavy +5

Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings. While the selection of state observations is crucial, there…

cs.RO2023★ 6 cited

DeepTransition: Viability Leads to the Emergence of Gait Transitions in Learning Anticipatory Quadrupedal Locomotion Skills

Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert

Quadruped animals seamlessly transition between gaits as they change locomotion speeds. While the most widely accepted explanation for gait transitions is energy efficiency, there…

cs.RO2023

Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive

Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert

Quadruped animal locomotion emerges from the interactions between the spinal central pattern generator (CPG), sensory feedback, and supraspinal drive signals from the brain. Comput…