The recent advancements in robotic manipulation have shown a significant shift towards integrating tactile feedback and advanced planning algorithms to enhance performance in complex, contact-rich tasks. A notable trend is the development of systems that leverage both visual and tactile data to ensure safety and precision in dynamic environments. For instance, methods like SafeDiff incorporate real-time tactile feedback to refine state planning, ensuring force safety during manipulation. Another direction is the use of model-based planning to generate training data for dexterous manipulation, as seen in approaches that modify sampling-based planners to produce consistent yet diverse demonstrations. Additionally, there is a growing emphasis on creating adaptable robotic hands with high-resolution tactile sensing, exemplified by the F-TAC Hand, which demonstrates superior performance in dynamic grasping tasks due to its rich tactile feedback. These innovations collectively push the boundaries of robotic manipulation, enabling more robust and human-like interactions in complex environments.

Noteworthy papers include one that introduces a diffusion-based goal-conditioned behavior cloning approach for contact-rich manipulation, and another that presents a novel state diffusion framework integrating tactile feedback for force safety in vision-guided manipulation.