What Would You Do? Acting by Learning to Predict
This work reduces priors needed for robot task learning compared to existing methods like Learning from Demonstration, addressing a domain-specific challenge in robotics.
The authors tackled the problem of enabling robots to learn tasks directly from visual demonstrations by predicting action outcomes, achieving generalization to unseen states in table-top object manipulation tasks.
We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perform a human demonstrated task without knowledge of the thought processes or actions of the human, only their visually observable state transitions. We evaluate our approach on two table-top, object manipulation tasks and demonstrate generalisation to previously unseen states. Our approach reduces the priors required to implement a robot task learning system compared with the existing approaches of Learning from Demonstration, Reinforcement Learning and Inverse Reinforcement Learning.