We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon tasks over a large workspace. Our system is modular: it deploys state of the art Large Language Models for task planning,Vision-Language models for semantic perception, and Point Cloud transformers for grasping. With semantic and physical safety in mind, these modules are interfaced with a real-time trajectory optimizer and a compliant tracking controller to enable human-robot proximity. We demonstrate performance for the following tasks: bi-arm sorting, bottle opening, and trash disposal tasks. These are done zero-shot where the models used have not been trained with any real world data from this bi-arm robot, scenes or workspace. Composing both learning- and non-learning-based components in a modular fashion with interpretable inputs and outputs allows the user to easily debug points of failures and fragilities. One may also in-place swap modules to improve the robustness of the overall platform, for instance with imitation-learned policies. Please see https://sites.google.com/corp/view/safe-robots .
@article{arxiv.2404.03570,
title = {Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity},
author = {Jake Varley and Sumeet Singh and Deepali Jain and Krzysztof Choromanski and Andy Zeng and Somnath Basu Roy Chowdhury and Avinava Dubey and Vikas Sindhwani},
journal= {arXiv preprint arXiv:2404.03570},
year = {2024}
}