ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning

1 code implementation • 28 Jun 2024 • Christopher E. Mower, Yuhui Wan, Hongzhan Yu, Antoine Grosnit, Jonas Gonzalez-Billandon, Matthieu Zimmer, Jinlong Wang, Xinyu Zhang, Yao Zhao, Anbang Zhai, Puze Liu, Daniel Palenicek, Davide Tateo, Cesar Cadena, Marco Hutter, Jan Peters, Guangjian Tian, Yuzheng Zhuang, Kun Shao, Xingyue Quan, Jianye Hao, Jun Wang, Haitham Bou-Ammar

Key features of the framework include: integration of ROS with an AI agent connected to a plethora of open-source and commercial LLMs, automatic extraction of a behavior from the LLM output and execution of ROS actions/services, support for three behavior modes (sequence, behavior tree, state machine), imitation learning for adding new robot actions to the library of possible actions, and LLM reflection via human and environment feedback.

AI Agent Imitation Learning

ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction

no code implementations • 13 Oct 2022 • Christopher E. Mower, Theodoros Stouraitis, João Moura, Christian Rauch, Lei Yan, Nazanin Zamani Behabadi, Michael Gienger, Tom Vercauteren, Christos Bergeles, Sethu Vijayakumar

However, there is a lack of software connecting reliable contact simulation with the larger robotics ecosystem (i. e. ROS, Orocos), for a more seamless application of novel approaches, found in the literature, to existing robotic hardware.