Robot manipulation systems often face challenges in adapting to real-world environments due to factors like changing objects, lighting, and contact dynamics. To address these issues, NVIDIA Robotics Research and Development Digest explores innovative methods such as reasoning large language models (LLMs), sim-and-real co-training, and vision-language models (VLMs) for designing tools. The ThinkAct framework enhances robot reasoning and action execution by integrating high-level reasoning with low-level action-execution, ensuring robots can plan and adapt to diverse tasks. Sim-and-real policy co-training helps bridge the gap between simulation and real-world applications by aligning observations and actions, while RobotSmith uses VLMs to automatically design task-specific tools. The Cosmos Cookbook provides open-source resources to further improve robot manipulation skills by offering examples and workflows for deploying Cosmos models. This matters because advancing robot manipulation capabilities can significantly enhance automation and efficiency in various industries.
