O2A: One-shot Observational learning with Action vectors
This enables robots to learn tasks from minimal human demonstrations, addressing a key bottleneck in robotic imitation learning, though it is incremental as it builds on existing methods like reinforcement learning and pre-trained models.
The paper tackles the problem of learning robotic manipulation tasks from a single third-person demonstration video, achieving performance comparable to an oracle under various domain shifts.
We present O2A, a novel method for learning to perform robotic manipulation tasks from a single (one-shot) third-person demonstration video. To our knowledge, it is the first time this has been done for a single demonstration. The key novelty lies in pre-training a feature extractor for creating a perceptual representation for actions that we call 'action vectors'. The action vectors are extracted using a 3D-CNN model pre-trained as an action classifier on a generic action dataset. The distance between the action vectors from the observed third-person demonstration and trial robot executions is used as a reward for reinforcement learning of the demonstrated task. We report on experiments in simulation and on a real robot, with changes in viewpoint of observation, properties of the objects involved, scene background and morphology of the manipulator between the demonstration and the learning domains. O2A outperforms baseline approaches under different domain shifts and has comparable performance with an oracle (that uses an ideal reward function).