From the 1 of 7 linked papers with an AI index.
7 papers
Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
Yi Wang, Wendi Chen, Zimo Wen +8
The paper introduces LIFT, a post‑training method that adds reactive force feedback to pretrained vision‑language‑action policies, enabling them to handle contact‑rich manipulation…
FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
Chengbo Yuan, Zicheng Zhang, Mingjie Zhou +14
Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups. This is because tactile signals…
When Backdoors Meet Partial Observability: Attacking Real-World Reinforcement Learning
Tairan Huang, Qingqing Ye, Yulin Jin +4
Backdoor attacks can cause reinforcement learning (RL) policies to behave normally under clean inputs while executing malicious behaviors when triggers are present. Existing RL bac…
RoboPocket: Improve Robot Policies Instantly with Your Phone
Junjie Fang, Wendi Chen, Han Xue +7
Scaling imitation learning is fundamentally constrained by the efficiency of data collection. While handheld interfaces have emerged as a scalable solution for in-the-wild data acq…
Genie Centurion: Accelerating Scalable Real-World Robot Training with Human Rewind-and-Refine Guidance
Wenhao Wang, Jianheng Song, Chiming Liu +13
While Vision-Language-Action (VLA) models show strong generalizability in various tasks, real-world deployment of robotic policy still requires large-scale, high-quality human expe…
SOP: A Scalable Online Post-Training System for Vision-Language-Action Models
Mingjie Pan, Siyuan Feng, Qinglin Zhang +9
Vision-language-action (VLA) models achieve strong generalization through large-scale pre-training, but real-world deployment requires expert-level task proficiency in addition to…