arXiv:2607. 12114v1 Announce Type: cross Abstract: A humanoid that can walk should not relearn locomotion from scratch to jog or run.
By Kwan-Yee Lin, Zilin Wang, Janelle J. Liu, Stella X. Yu
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
arXiv:2607. 29172v1 Announce Type: cross Abstract: While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users' ability to adapt them to new tasks, embodiments, and deployment settings.
By Yuxin Chen, Hari Srikanth, Nathan Jew, Menglin Wu, Pengcheng Wang, Junli Ren, Masayoshi Tomizuka, Peng Xu, Jinyu Xie, Thomas Tian
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:2607. 24083v1 Announce Type: new Abstract: Reinforcement learning can produce robust humanoid controllers, but each new task is typically trained as a separate policy with its own reward design and training process.
By Valerio Belli (UNIROMA, UCL), Valerio Modugno (UCL), Enrico Mingo Hoffman (HUCEBOT), Fabio Amadio (HUCEBOT)
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)