The field of robotics is moving towards the integration of multimodal inputs and dexterous manipulation capabilities, enabling robots to perform complex tasks in dynamic environments. Recent developments have focused on leveraging visual, textual, and tactile information to improve decision-making and control in autonomous systems. The use of pre-trained models and multimodal fusion strategies has shown promising results in enhancing agent learning efficiency and generalization. Additionally, the study of human tool-use and bimanual coordination has provided valuable insights into the development of more effective robotic manipulation policies. Noteworthy papers include:

• MORAL, which proposes a multimodal reinforcement learning framework for decision making in autonomous laboratories, achieving a 20% improvement in task completion rates.
• Tool-as-Interface, which introduces a framework for transferring tool-use knowledge from human data to robots, resulting in a 71% higher average success rate compared to diffusion policies.
• MAPLE, which leverages manipulation priors learned from large-scale egocentric video datasets to improve policy learning for dexterous robotic manipulation tasks, demonstrating effectiveness across simulation and real-world experiments.