AR/VR interfaces for robotic teleoperation and interaction.
Satoshi Yamamori, Koji Ishihara, Kentaro Minamikawa et al.
Instead of making simulation imitate every motor imperfection, this work shapes the physical actuator to follow the simulated reference.
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.
Hexian Ni, Tao Lu, Yinghao Cai
CoRe combines code-based rewards with learned visual preferences so that neither reward source has to capture the entire task alone.
Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha et al.
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.
Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha et al.
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.
Jianing Guo, Fangzheng Chen, Zihao Mao et al.
Flow matching has emerged as a standard paradigm for robotic manipulation owing to its strong expressive power for modelling complex, multimodal action distributions, alongside similar approaches like diffusion policy.
Jianing Guo, Fangzheng Chen, Zihao Mao et al.
Flow matching has emerged as a standard paradigm for robotic manipulation owing to its strong expressive power for modelling complex, multimodal action distributions, alongside similar approaches like diffusion policy.
Chenyang Ma, Yue Yang, Radu Corcodel et al.
FurnitureVLA makes assembly progress an explicit prediction, helping a bimanual policy decide when to move to the next subtask.
Chi Zhang, Penglin Cai, Ziheng Xi et al.
TTP transfers tactile experience from human demonstrations into robot policies for contact-sensitive manipulation.
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.
Sizhe Yang, Juncheng Mu, Tianming Wei et al.
Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling.
Sizhe Yang, Juncheng Mu, Tianming Wei et al.
Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling.
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.
Kinam Kim, Namiko Saito, Heecheol Kim et al.
Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks, but their imitation-learning-based policies remain brittle in precise physical interactions due to compounding execution errors; Can a reinforcement learning policy trained purely in simulation improve the robustness of real-world VLAs zero-shot?
Kinam Kim, Namiko Saito, Heecheol Kim et al.
Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks, but their imitation-learning-based policies remain brittle in precise physical interactions due to compounding execution errors; Can a reinforcement learning policy trained purely in simulation improve the robustness of real-world VLAs zero-shot?
Yuwan Liu, Hongze Yu, Song Liu et al.
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification.
Arnav Balaji, Arpit Bahety, Sriniket Ambatipudi et al.
While robotic manipulation capabilities have advanced rapidly, physical safety remains a major barrier to deploying household robots: task success is insufficient if the robot damages itself or its surroundings.
Jonghoon Lee, Seong Hyeon Park, Byungwoo Jeon et al.
Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or geometry deviates from the training distribution.
Jonghoon Lee, Seong Hyeon Park, Byungwoo Jeon et al.
Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or geometry deviates from the training distribution.
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 ar-vr.
Satoshi Yamamori, Koji Ishihara et al. · 2026
Yandong Wang, Jiaqian Yu et al. · 2026
Yandong Wang, Jiaqian Yu et al. · 2026
Hexian Ni, Tao Lu et al. · 2026
Gia-Binh Nguyen, Trong-Bao Ho et al. · 2026
Gia-Binh Nguyen, Trong-Bao Ho et al. · 2026
…and 18 more papers.
