The field of robotics is moving towards more advanced and nuanced manipulation capabilities, with a focus on tactile sensing and dexterous manipulation. Recent research has explored the use of tactile feedback to improve grasping and manipulation, as well as the development of more sophisticated robotic hands and fingers. One notable trend is the integration of tactile sensing with vision and other modalities to enable more robust and adaptive manipulation. Another area of focus is the development of more efficient and effective algorithms for grasp planning and execution, including the use of machine learning and optimization techniques.

Noteworthy papers in this area include: High-Bandwidth Tactile-Reactive Control for Grasp Adjustment, which proposes a purely tactile-feedback grasp-adjustment algorithm that can refine a grasp even when starting from a crude initial configuration. DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation, which introduces a soft, conformable capacitive electronic skin that enables sensitive and localized tactile sensing. BiGraspFormer: End-to-End Bimanual Grasp Transformer, which proposes a unified end-to-end transformer framework that directly generates coordinated bimanual grasps from object point clouds.