  Reinforcement Learning (RL) and Imitation Learning (IL) have made great
progress in robotic control in recent years. However, these methods show
obvious deterioration for new tasks that need to be completed through new
combinations of actions. RL methods heavily rely on reward functions that
cannot generalize well for new tasks, while IL methods are limited by expert
demonstrations which do not cover new tasks. In contrast, humans can easily
complete these tasks with the fragmented knowledge learned from task-agnostic
experience. Inspired by this observation, this paper proposes a task-agnostic
learning method (TAL for short) that can learn fragmented knowledge from
task-agnostic data to accomplish new tasks. TAL consists of four stages. First,
the task-agnostic exploration is performed to collect data from interactions
with the environment. The collected data is organized via a knowledge graph.
Compared with the previous sequential structure, the knowledge graph
representation is more compact and fits better for environment exploration.
Second, an action feature extractor is proposed and trained using the collected
knowledge graph data for task-agnostic fragmented knowledge learning. Third, a
candidate action generator is designed, which applies the action feature
extractor on a new task to generate multiple candidate action sets. Finally, an
action proposal is designed to produce the probabilities for actions in a new
task according to the environmental information. The probabilities are then
used to select actions to be executed from multiple candidate action sets to
form the plan. Experiments on a virtual indoor scene show that the proposed
method outperforms the state-of-the-art offline RL method: CQL by 35.28% and
the IL method: BC by 22.22%.
