The field of dexterous manipulation is moving towards developing more advanced and robust methods for robotic hands to interact with and manipulate objects. Recent research has focused on improving the ability of robots to learn from demonstrations and adapt to new situations, with a particular emphasis on imitation learning and reinforcement learning. One of the key challenges in this area is the need for more efficient and effective ways to collect and utilize data, as well as the development of more sophisticated sensors and hardware.

Notable papers in this area include the presentation of the ORCA hand, a reliable and anthropomorphic robotic hand that can be assembled in less than eight hours and is capable of performing a variety of tasks. Another significant contribution is the development of a vibration-based adhesion system for miniature wall-climbing robots, which has the potential to enable more efficient and compact designs. The introduction of the Wavelet Policy, a novel approach to imitation learning that utilizes wavelet transforms to extract multi-scale features from the frequency domain, has also shown promising results. Additionally, the RobustDexGrasp framework has demonstrated strong generalization in grasping unseen objects with random poses, achieving success rates of 97.0% across 247,786 simulated objects and 94.6% across 512 real objects.