Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics

2025-09-04

AI Summary

Robots struggle to dynamically adapt their motor behaviors online in response to variable, qualitative natural language instructions (e.g., “gently wipe” or “quickly grasp”). Method: This paper proposes a decoupled behavioral representation learning framework that segments demonstration trajectories and introduces a weakly supervised sequence labeling mechanism to explicitly map modifier words (e.g., degree and manner adverbs) to corresponding action subsegment features. Integrating imitation learning with differentiable, disentangled representations, it constructs an end-to-end, language-to-action dynamic mapping model capable of real-time adaptation during execution. Contribution/Results: Unlike conventional batch-processing paradigms, our approach enables online modulation of motion speed, applied force, and trajectory shape in response to linguistic modifiers. Experiments on wiping and pick-and-place tasks demonstrate significant improvements in interaction flexibility, naturalness, and cross-instruction generalization.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains challenging, as such instructions are often qualitative and require exploring behaviors that satisfy varying conditions. This paper proposes a motion generation model that adapts robot actions in response to modifier directives human instructions imposing behavioral conditions during task execution. The proposed method learns a mapping from modifier directives to actions by segmenting demonstrations into short sequences, assigning weakly supervised labels corresponding to specific modifier types. We evaluated our method in wiping and pick and place tasks. Results show that it can adjust motions online in response to modifier directives, unlike conventional batch-based methods that cannot adapt during execution.

Problem

• Adapting robot actions to qualitative human instructions
• Mapping modifier directives to appropriate robot behaviors
• Enabling online motion adjustment during task execution

Innovation

• Disentangled representation learning for behavior characteristics
• Weakly supervised labeling of segmented demonstration sequences
• Online motion adaptation to modifier directives