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:2606. 19633v1 Announce Type: cross Abstract: Perceptive legged locomotion over discontinuous terrain (e.
By Francisco Affonso, Matheus P. Angarola, Ana Luiza Mineiro, Aditya Potnis, Marcelo Becker, Girish Chowdhary
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)
The paper introduces ASTRIL-MPC, a language‑guided neural model predictive control framework that enables articulated tracked robots to navigate complex, contact‑rich urban environments such as stairwells and cluttered interiors. By combining a learned kinematics model that predicts short‑horizon state changes, an optimization‑based planner with multi‑objective costs, and a large language model that safely updates control weights, the system achieves up to 71% better traversal quality than non‑adaptive NMPC and 67% better than a PPO baseline, while eliminating collision impacts during descent. Real‑robot trials over four indoor obstacles confirm the method’s transferability to physical contact‑rich traversal.
By Zhenfeng Gan, Yanbo Chen, Lirong Che, Yongyi Ma, Rongkai Zhu, Xueqian Wang
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. 11891v1 Announce Type: cross Abstract: Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy.
By Mehmet Turan Yard{\i}mc{\i}