Robotics with Large Language Models

Leveraging LLMs for robotic planning, control, and human-robot interaction

This research explores how large language models can be integrated into robotic systems to enable more intelligent, flexible, and natural human-robot interaction. By combining the reasoning capabilities of LLMs with robotic perception and control, we aim to create systems that can understand complex instructions and adapt to diverse environments.

Research Vision

The integration of LLMs into robotics represents a paradigm shift in how robots understand tasks, plan actions, and interact with humans. Our work focuses on making robots more accessible and capable through natural language interfaces.

Key Research Directions

Task Planning and Reasoning

Using LLMs to:

  • Decompose complex high-level instructions into executable robot actions
  • Reason about task constraints and environmental conditions
  • Generate and evaluate alternative action plans
  • Handle ambiguity and uncertainty in task specifications

Grounded Language Understanding

Connecting language to the physical world:

  • Mapping natural language descriptions to visual scenes
  • Understanding spatial relationships and object references
  • Grounding abstract concepts in robot perception
  • Handling context-dependent language interpretations

Human-Robot Interaction

Enabling natural communication:

  • Processing free-form natural language commands
  • Generating explanations of robot actions and decisions
  • Asking clarifying questions when instructions are ambiguous
  • Adapting to different communication styles

Technical Challenges

Our research addresses:

  1. Grounding Problem: Connecting symbolic LLM outputs to continuous robot control
  2. Safety and Reliability: Ensuring LLM-generated plans are safe and executable
  3. Real-time Performance: Achieving low-latency responses for interactive scenarios
  4. Domain Adaptation: Specializing general-purpose LLMs for robotic contexts

Applications

This work enables robots to:

  • Assist in household and service tasks through natural language interaction
  • Collaborate with humans in manufacturing and assembly
  • Operate in unstructured environments with minimal programming
  • Learn new tasks from human instruction and demonstration

References