Flexible Language-Guided Representation Editing for Adaptive Robot Behavior

Key points

• Trajectory demonstrations show robots how to behave but not why, leaving them to guess which features of the task actually matter.1
• Incorrect guesses produce misaligned internal representations and lead to incorrect robot behaviors.1
• Because task features are often semantically defined, we argue that natural language is better suited for specifying features rather than relying on demonstrations.1
• We evaluate FLEX on 2D navigation scenarios, 7DoF Franka tabletop manipulation tasks, and a real-world study using naturalistic human language corrections and real-robot demonstrations.1

Sources (1)

• [1]Flexible Language-Guided Representation Editing for Adaptive Robot Behavior
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 08:16 PM

  We propose the Flexible Feature Editing Pipeline (FLEX), which uses language models to iteratively refine a robot's internal representation of task features from natural language corrections via three complementary operations: Add missing features, Generalize existing specific features, and Mask irrelevant features.

  Trajectory demonstrations show robots how to behave but not why, leaving them to guess which features of the task actually matter.

Extractive summary: sentences quoted from the sources.