no code implementations • 16 Oct 2023 • 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.
no code implementations • 29 Jun 2023 • Wanming Yu, Chuanyu Yang, Christopher McGreavy, Eleftherios Triantafyllidis, Guillaume Bellegarda, Milad Shafiee, Auke Jan Ijspeert, Zhibin Li
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings.
no code implementations • 12 Jun 2023 • Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert
Consistent with quadruped animal data, we show that the walk-trot gait transition for quadruped robots on flat terrain improves both viability and energy efficiency.
no code implementations • 26 Feb 2023 • Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert
Moreover, our investigation shows that sensing the front feet distances to the gap is the most important and sufficient sensory information for learning gap crossing.
no code implementations • 29 Dec 2022 • Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert
2) What are the effects of using a memory-enabled vs. a memory-free policy network with respect to robustness, energy-efficiency, and tracking performance in sim-to-real navigation tasks?
no code implementations • 1 Nov 2022 • 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 produce robust and omnidirectional quadruped locomotion.
no code implementations • 11 Mar 2021 • Guillaume Bellegarda, Yiyu Chen, Zhuochen Liu, Quan Nguyen
Policies can be learned in only a few million time steps, even for challenging tasks of running over rough terrain with loads of over 100% of the nominal quadruped mass.
no code implementations • 13 Nov 2020 • Guillaume Bellegarda, Chuong Nguyen, Quan Nguyen
In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments, such as off of uneven terrain and with variable robot dynamics parameters.
no code implementations • 21 Oct 2019 • Guillaume Bellegarda, Katie Byl
Recent breakthroughs both in reinforcement learning and trajectory optimization have made significant advances towards real world robotic system deployment.
no code implementations • 6 Mar 2019 • Guillaume Bellegarda, Katie Byl
Recent breakthroughs in the reinforcement learning (RL) community have made significant advances towards learning and deploying policies on real world robotic systems.