JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy
This addresses the challenge of insufficient data diversity and poor cross-embodiment generalization for robotic manipulation, representing a novel method rather than an incremental improvement.
The paper tackles the problem of robotic autonomy in open-world environments by proposing JoyAI-RA, a vision-language-action foundation model that integrates multi-source data to bridge embodiment gaps, resulting in outperforming state-of-the-art methods in simulation and real-world benchmarks.
Robotic autonomy in open-world environments is fundamentally limited by insufficient data diversity and poor cross-embodiment generalization. Existing robotic datasets are often limited in scale and task coverage, while relatively large differences across robot embodiments impede effective behavior knowledge transfer. To address these challenges, we propose JoyAI-RA, a vision-language-action (VLA) embodied foundation model tailored for generalizable robotic manipulation. JoyAI-RA presents a multi-source multi-level pretraining framework that integrates web data, large-scale egocentric human manipulation videos, simulation-generated trajectories, and real-robot data. Through training on heterogeneous multi-source data with explicit action-space unification, JoyAI-RA effectively bridges embodiment gaps, particularly between human manipulation and robotic control, thereby enhancing cross-embodiment behavior learning. JoyAI-RA outperforms state-of-the-art methods in both simulation and real-world benchmarks, especially on diverse tasks with generalization demands.