  Effective execution of long-horizon tasks with dexterous robotic hands
remains a significant challenge in real-world problems. While learning from
human demonstrations have shown encouraging results, they require extensive
data collection for training. Hence, decomposing long-horizon tasks into
reusable primitive skills is a more efficient approach. To achieve so, we
developed DexSkills, a novel supervised learning framework that addresses
long-horizon dexterous manipulation tasks using primitive skills. DexSkills is
trained to recognize and replicate a select set of skills using human
demonstration data, which can then segment a demonstrated long-horizon
dexterous manipulation task into a sequence of primitive skills to achieve
one-shot execution by the robot directly. Significantly, DexSkills operates
solely on proprioceptive and tactile data, i.e., haptic data. Our real-world
robotic experiments show that DexSkills can accurately segment skills, thereby
enabling autonomous robot execution of a diverse range of tasks.
