Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
This work addresses the challenge of data inefficiency in robot task learning for sequential tasks with critical subgoals, though it is incremental as it builds on existing IRL and HITL frameworks.
The paper tackles the problem of inefficient learning in inverse reinforcement learning by incorporating human-provided subgoals and interactive partial demonstrations, resulting in significantly more efficient learning that requires only a fraction of the demonstration data compared to baseline methods.
Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.