Advancing Robotic Natural Language Understanding Through Semantic Parsing, Dialog, And Multi-modal Perception.pdf

Continuously Improving Natural Language Understanding for Robotic Systems

This dissertation proposal outlines a comprehensive approach to enhance natural language understanding in robotic systems. The goal is to enable robots to interpret and execute commands from untrained human users, even in dynamic environments.

Key Components:

1. Semantic Parsing: Translating natural language utterances into machine-understandable semantic representations (e.g., the example "go(the(λx.(office(x) ∧owns(alice, x)))) ∧deliver(the(λy.(light2(y) ∧mug1(y))), bob)" ).

2. Dialog and Human-Robot Interaction: Using conversation to refine semantic parsers by actively seeking clarification and generating more training data (e.g., "I Spy" games).

3. Multi-modal Perception: Grounding linguistic concepts in sensory perception, allowing robots to understand object properties like "green" or "heavy" based on visual or tactile input.

4. Word Sense Synonym Set Induction: Discovering synonyms and polysemy (multiple meanings) within words to improve robustness in the face of ambiguity ("light" can refer to color or weight).

Proposed Integration:

The proposal integrates these components into a cohesive robotic system:

Short-Term Work: Focuses on applying existing techniques for synonym set induction, grounding semantic parses against knowledge and perception, and building upon ongoing work in "I Spy" based learning.

Long-Term Vision: Aims to create a fully integrated system that continuously learns from human interaction, improving both its language understanding (parsing) and perception capabilities over time.

Benefits:

This research aims to:

Enhance robot's ability to understand complex natural language commands.
Improve robustness in handling ambiguous words and synonyms.
Leverage human-robot interaction for active learning, leading to more accurate and adaptable systems.
Enable robots to operate effectively in diverse, dynamic environments populated by untrained humans.