Aerial grasping is a remarkable capability exhibited by predatory birds, allowing them to capture prey through highly coordinated maneuvers in flight. Inspired by this capability, researchers have developed various formulations to reproduce such maneuvers through trajectory optimization. However, two limitations remain...
Wei-Liang Deng, Zheng-Yang Dang, Yao Mu et al.· 0 citations
RoboFoundry is proposed, the first embodied agentic framework that formulates this process of system-as-policy evolution across foundation models as Self-Evolving System-as-Policy, highlighting its potential for fully autonomous embodied agents.
Jing-Song Liang, Shu-Hao Liao, Shi-Zhe Zhang et al.· 0 citations
Xpolicylab is released as shared infrastructure for reproducible policy comparison and standardized deployment across simulation and physical platforms for reproducible policy comparison and standardized deployment across simulation and physical platforms.
This work organizes the embodied data ecosystem as a pyramidspanning five complementary sources: real-robot data, UMI-style data, egocentric and exocentric data, simulation data, and general vision-language data, and further characterize each source in terms of data quality, diversity, reusability, and physical fidelit...
Yifan Ye, Yankai Fu, Ya-hui Lv et al.· arXiv.org· 4 citations
Experiments show that DenseReward outperforms general-purpose VLMs and existing robotic reward models in dense reward prediction across both simulated and real-world manipulation, and provides effective reward guidance for downstream model predictive control and reinforcement learning.
Yu Fang, Wanxi Dong, Jiaqi Liu et al.· arXiv.org· 1 citation
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