According to @StanfordAILab, the Stanford AI Lab community has presented more than 20 research papers at CoRL 2025, highlighting significant advancements in robotics, machine learning, and autonomous systems (source: https://ai.stanford.edu/blog/corl-2025/). These papers cover practical applications in robotic manipulation, reinforcement learning, and human-robot interaction, offering new insights for businesses seeking to leverage AI-driven automation. The breadth of research demonstrates Stanford's leadership in AI innovation, and provides industry stakeholders with actionable findings to accelerate adoption of cutting-edge AI technologies in real-world settings (source: @corl_conf).

According to @physical_int, a comprehensive set of AI behavioral cloning baselines—including Diffusion Policy, WB-VIMA, ACT, BC-RNN, as well as pre-trained VLA models like OpenVLA and π_0—has been provided to accelerate robotics research and experimentation. These baseline models represent state-of-the-art approaches in imitation learning, enabling researchers to quickly benchmark and iterate on new algorithms. The inclusion of both classic and pre-trained models supports rapid development and evaluation of AI-driven robotic policies, ultimately lowering the barrier to entry for innovation in robotics and AI applications (source: @physical_int, Twitter).