Generation models for robotics.
Yandong Wang, Jiaqian Yu, Xiongfeng Peng et al.
Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end learning, leveraging large vision-language backbones with continuous action prediction.
Yandong Wang, Jiaqian Yu, Xiongfeng Peng et al.
Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end learning, leveraging large vision-language backbones with continuous action prediction.
Yu Zhang, Kangyi Ji, Yongxiang Zou et al.
This paper presents an invertible neural network adapter for general robotic manipulation, designed to generate precise high-dimensional actions conditioned on multimodal observations, including visual, linguistic, and proprioceptive inputs, through a one-step denoising process.
Yu Zhang, Kangyi Ji, Yongxiang Zou et al.
This paper presents an invertible neural network adapter for general robotic manipulation, designed to generate precise high-dimensional actions conditioned on multimodal observations, including visual, linguistic, and proprioceptive inputs, through a one-step denoising process.
William English, Hao Zheng, Rickard Ewetz
This method checks a predicted action sequence for future collisions while the VLA is still generating it, rather than correcting only the next action.
Siqiao Huang, Kun-Ying Lee, Dongming Qiao et al.
Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy reward engineering, or motion trackers that are difficult to extend to new input modalities.
Arunim Joarder, Arjun Bhardwaj, René Zurbrügg et al.
Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation.
Arunim Joarder, Arjun Bhardwaj, René Zurbrügg et al.
Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation.
Yuquan Xue, Le Xu, Zeyi Liu et al.
Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.
Read these papers in order to build expertise in generation.
Yandong Wang, Jiaqian Yu et al. · 2026
Yandong Wang, Jiaqian Yu et al. · 2026
Yu Zhang, Kangyi Ji et al. · 2026
Yu Zhang, Kangyi Ji et al. · 2026
William English, Hao Zheng et al. · 2026
Siqiao Huang, Kun-Ying Lee et al. · 2026
…and 3 more papers.
