Prépublication
· 2026 arXiv
EN
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then...
Botao He, Yiming Dou et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control networks are typically obtained through conventional training procedures, while comparatively l...
Huixuan Cheng, Bike Zhang et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-...
Zhefei Gong, Hefeifei Jiang et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Task planning for robots in off-site construction prefabrication is primarily manual, leading to planning inefficiencies and reluctance to adopt robotic solutions. Existing approaches provide limited support for scalable, modular automated task planning and lo...
Shermin Sherkat, Thomas Wortmann et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-horizon trajectories due to context-length limits, redundant historical observations, and unstable spatial al...
Xueyao Wei, Yifei Wang et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish tha...
Jingwei Song, Zhisheng Chen et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse. Robot agents that act through code can already repair programs from execution fe...
Zimo Wen, Yijin Chen et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
World action models (WAMs) predict actions and future observations, typically from a reconstruction-based representation that carries noisy, redundant information which can complicate downstream predictions. We introduce LeWAM, a bidirectional transformer for...
Shashank Hegde, Alexander Popov et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control c...
Wei Huang, Bohan Zhang et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-language models they are built on. A one-word edit can move success by tens of points: $π_{0.5}$ turns on a LIBERO st...
Mikey Watts, Yuchen Cui
#cs.RO
Prépublication
· 2026 arXiv
EN
We present FoldBack, a self-correcting masked generative policy for long-horizon garment folding. Existing long-trajectory policies may continue after a missed or slipped grasp even when the garment has not reached the intended configuration. We structure Fold...
Lipeng Zhuang, Shiyu Fan et al.
#cs.RO