  Learning from human demonstrations has exhibited remarkable achievements in
robot manipulation. However, the challenge remains to develop a robot system
that matches human capabilities and data efficiency in learning and
generalizability, particularly in complex, unstructured real-world scenarios.
We propose a system that processes RGBD videos to translate human actions to
robot primitives and identifies task-relevant key poses of objects using
Grounded Segment Anything. We then address challenges for robots in replicating
human actions, considering the human-robot differences in kinematics and
collision geometry. To test the effectiveness of our system, we conducted
experiments focusing on manual dishwashing. With a single human demonstration
recorded in a mockup kitchen, the system achieved 50-100% success for each step
and up to a 40% success rate for the whole task with different objects in a
home kitchen. Videos are available at https://robot-dishwashing.github.io
