One-Shot Observation Learning Using Visual Activity Features

Observation learning is the process of learning a task by observing an expert demonstrator. Our principal contribution is a one-shot learning method for robot manipulation tasks in which only a single demonstration is required. The key idea is to encode the demonstration in an activity space defined as part of a previously trained activity classifier. The distance between this encoding and equivalent encodings from trials of a robot performing the same task provides a reward function supporting iterative learning of task completion by the robotic manipulator. We use reinforcement learning for experiments with a simulated robotic manipulator, and stochastic trajectory optimisation for experiments with a real robotic manipulator. We show that the proposed method can be used to learn tasks from a single demonstration under varying viewpoint of observation, object properties, scene background and morphology of the manipulator. Videos of all results, including demonstrations, can be found on: https://tinyurl.com/s2l-stage1