The field of robot manipulation is witnessing significant developments, driven by advancements in vision-language models. Researchers are focusing on enhancing the cognitive capabilities of robots to perform complex tasks, such as tool use and grasping, in diverse environments. A key direction is the integration of multimodal instructions, including images and text, to improve the flexibility and robustness of robotic systems. Additionally, the use of synthetic data and simulated environments is being explored to mitigate the limitations of real-world data collection and improve the scalability of embodied foundation models. Noteworthy papers include:

• Dynamic Robot Tool Use with Vision Language Models, which introduces a novel framework for inverse tool-use planning, enabling fine-grained planning for versatile robotic tool use.
• CrayonRobo, which leverages comprehensive multi-modal prompts to convey low-level actions and high-level planning for robotic manipulation tasks.
• Interleave-VLA, which presents a framework for comprehending interleaved image-text instructions and generating continuous action sequences in the physical world.
• GraspVLA, which explores the feasibility of training vision-language-action models entirely with large-scale synthetic action data, achieving advanced zero-shot generalizability and few-shot adaptability.