The field of robotic manipulation is moving towards more generalizable and scalable solutions, with a focus on vision-language-action (VLA) models. Recent developments have shown that pretraining VLA models on large datasets of human activity videos can lead to strong zero-shot capabilities and improved task success rates in real-world robotic experiments. Additionally, there is a growing interest in applying VLA models to assistive tasks, such as feeding and cleaning, with the goal of making robotic solutions more accessible and affordable. Noteworthy papers in this area include:

• A paper that presents a novel approach for pretraining VLA models using unscripted real-life video recordings of human hand activities, achieving state-of-the-art results in task success rates and generalization to novel objects.
• A paper that introduces a low-cost robotic arm for assistive tasks, using imitation learning from demonstration videos and achieving over 90% task accuracy.
• A paper that proposes a novel framework for synthesizing hand language manipulation for articulated objects, achieving superior hand grasp sequence generation performance compared to state-of-the-art methods.