6 papers
Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation
Shukai Gong, Xuanran Zhai, Yintianrun Zhang +16
Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordinated subtasks. Existing worl…
HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining
Juncheng Ma, Jianxin Bi, Yufan Deng +19
Embodied foundation models are expected to benefit from data scaling like large language models, but face a much tighter data bottleneck. Teleoperated real-robot trajectories remai…
SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse
Xuanran Zhai, Zekai Huang, Longyan Wu +5
Recent progress in vision-language-action (VLA) models has demonstrated strong potential for dual-arm manipulation, enabling complex behaviors and generalization to unseen environm…
Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
Ce Hao, Xuanran Zhai, Yaohua Liu +1
Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size an…
Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation
Qianyou Zhao, Yuliang Shen, Xuanran Zhai +5
In visuomotor policy learning, diffusion-based imitation learning has become widely adopted for its ability to capture diverse behaviors. However, approaches built on ordinary and…
VFP: Variational Flow-Matching Policy for Multi-Modal Robot Manipulation
Xuanran Zhai, Qianyou Zhao, Qiaojun Yu +1
Flow-matching-based policies have recently emerged as a promising approach for learning-based robot manipulation, offering significant acceleration in action sampling compared to d…