7 papers
Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning
Wenke Xia, Pei Ren, Wenbo Yu +10
Offline-to-online reinforcement learning is promising for generalizable robotic manipulation, yet its full-stack complexity obscures reproduction and diagnosis. Within such systems…
VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis
Zeqin Liao, Peifan Ren, Zixu Gao +6
Vision-Language-Action (VLA) models follow a data-driven paradigm and are constrained by the coverage of training data, making them prone to failure on edge-case configurations aft…
RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks
Shiying Duan, Pei Ren, Nanxiang Jiang +5
Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios. While existing methods have achieved promising results in task planni…
RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence
Chengkai Hou, Kun Wu, Jiaming Liu +30
While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstration…
RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation
Yixue Zhang, Kun Wu, Zhi Gao +12
The pursuit of general-purpose robotic manipulation is hindered by the scarcity of diverse, real-world interaction data. Unlike data collection from web in vision or language, robo…
Real-world Reinforcement Learning from Suboptimal Interventions
Yinuo Zhao, Huiqian Jin, Lechun Jiang +9
Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn fro…