Improving Natural Language Interaction with Robots Using Advice
This addresses the challenge of improving human-robot communication for tasks like blocks world, though it is incremental as it builds on existing single-step interaction models.
The paper tackles the problem of low-bandwidth interaction in physically grounded language understanding by introducing a protocol for including advice to constrain agent predictions, showing that even simple advice leads to significant performance improvements in the blocks world task.
Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human operator gives an instruction and an automated agent is evaluated on its ability to execute it. In this paper we take the first step towards increasing the bandwidth of this interaction, and suggest a protocol for including advice, high-level observations about the task, which can help constrain the agent's prediction. We evaluate our approach on the blocks world task, and show that even simple advice can help lead to significant performance improvements. To help reduce the effort involved in supplying the advice, we also explore model self-generated advice which can still improve results.