13 citations · 25 across the 14 of their papers we have counts for
14 papers
SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
Peizhuo Li, Hongyi Li, Ge Sun +7
Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns. Most of these approaches use p…
AllGaits: Learning All Quadruped Gaits and Transitions
Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert
We present a framework for learning a single policy capable of producing all quadruped gaits and transitions. The framework consists of a policy trained with deep reinforcement lea…
Online Optimization of Central Pattern Generators for Quadruped Locomotion
Zewei Zhang, Guillaume Bellegarda, Milad Shafiee +1
Typical legged locomotion controllers are designed or trained offline. This is in contrast to many animals, which are able to locomote at birth, and rapidly improve their locomotio…
Dynamic Object Catching with Quadruped Robot Front Legs
André Schakkal, Guillaume Bellegarda, Auke Ijspeert
This paper presents a framework for dynamic object catching using a quadruped robot's front legs while it stands on its rear legs. The system integrates computer vision, trajectory…
Learning Human-Robot Handshaking Preferences for Quadruped Robots
Alessandra Chappuis, Guillaume Bellegarda, Auke Ijspeert
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 beco…
Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion
Ge Sun, Milad Shafiee, Peizhuo Li +3
Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in th…
Quadruped-Frog: Rapid Online Optimization of Continuous Quadruped Jumping
Guillaume Bellegarda, Milad Shafiee, Merih Ekin Özberk +1
Legged robots are becoming increasingly agile in exhibiting dynamic behaviors such as running and jumping. Usually, such behaviors are either optimized and engineered offline (i.e.…
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…
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…
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…
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…
Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotion
Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert
We present a framework for learning visually-guided quadruped locomotion by integrating exteroceptive sensing and central pattern generators (CPGs), i.e. systems of coupled oscilla…
CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion
Guillaume Bellegarda, Auke Ijspeert
In this letter, we present a method for integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to…
Experimental Design for Human-in-the-Loop Driving Simulations
Katherine Driggs-Campbell, Guillaume Bellegarda, Victor Shia +2
This report describes a new experimental setup for human-in-the-loop simulations. A force feedback simulator with four axis motion has been setup for real-time driving experiments.…