arXiv:2510. 18348v2 Announce Type: replace-cross Abstract: State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait priors that constrain the action space, bias policy optimization, and limit adaptability across robot morphologies, or (ii) operate "blind," making them unable to anticipate hind-leg terrain and brittle to observation noise.
By Alexandros Ntagkas, Chairi Kiourt, Konstantinos Chatzilygeroudis
arXiv:2607. 18365v1 Announce Type: cross Abstract: Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain.
By Jordan Dowdy, Jean Chagas Vaz
arXiv:2608. 02069v1 Announce Type: cross Abstract: Developing deployable locomotion policies through conventional reinforcement learning often requires complex reward engineering and expensive training times.
By Martin Opat
arXiv:2606. 04718v1 Announce Type: cross Abstract: Humans primarily rely on walking and running to traverse complex terrains, without resorting to unnecessarily complex motion patterns.
By Kailun Huang (Hong Kong University of Science and Technology), Zikang Xie (Hong Kong University of Science and Technology), Yanzhe Xie (Hong Kong University of Science and Technology), Panpan Liao (Guangdong University of Technology), Fanghai Zhang (Hong Kong University of Science and Technology), Yanheng Mai (Hong Kong University of Science and Technology), Wenhao Xu (South China Agricultural University), Yunheng Wang (Hong Kong University of Science and Technology), Renjing Xu (Hong Kong University of Science and Technology), Haohui Huang (Guangdong University of Technology)
arXiv:2607. 13579v1 Announce Type: cross Abstract: Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors.
By Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song, Joon-Ha Kim, Seungwoo Hong, Hae-Won Park
arXiv:2606. 15896v1 Announce Type: cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective.
By Loukas Kordos, Leonard T. Franz, Simon Rappenecker, Oliver Hausdoerfer, Angela P. Schoellig, Pavel Kolev, Georg Martius