Learning terrain-adaptive locomotion with agile behaviors by imitating animals

T Li, Y Zhang, C Zhang, Q Zhu, J Sheng… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
In this paper, we present a general learning framework for controlling a quadruped robot that
can mimic the behavior of real animals and traverse challenging terrains. Our method …

Optimizing bipedal maneuvers of single rigid-body models for reinforcement learning

R Batke, F Yu, J Dao, J Hurst, RL Hatton… - 2022 IEEE-RAS 21st …, 2022 - ieeexplore.ieee.org
In this work, we propose a method to generate reduced-order model reference trajectories
for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real …

[PDF][PDF] Deepreinforcementlearningforreal-world quadrupedal locomotion: a comprehensive review

H Zhang, L He, D Wang - 2022 - f.oaes.cc
Building controllers for legged robots with agility and intelligence has been one of the typical
challenges in the pursuit of artificial intelligence (AI). As an important part of the AI field …

Probabilistic homotopy optimization for dynamic motion planning

S Pardis, M Chignoli, S Kim - 2024 IEEE/RSJ International …, 2024 - ieeexplore.ieee.org
We present a homotopic approach to solving challenging, optimization-based motion
planning problems. The approach uses Homotopy Optimization, which, unlike standard …

Reinforcement learning for legged robots: Motion imitation from model-based optimal control

AJ Miller, S Fahmi, M Chignoli, S Kim - arxiv preprint arxiv:2305.10989, 2023 - arxiv.org
We propose MIMOC: Motion Imitation from Model-Based Optimal Control. MIMOC is a
Reinforcement Learning (RL) controller that learns agile locomotion by imitating reference …