AI FOR HUMAN-AI COORDINATION
Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
This paper introduces MIMIC, a novel framework that bridges cognitive science and imitation learning by operationalizing inner speech as a mediational mechanism between perception and action. MIMIC enables steerable generation of diverse behaviors and improves fidelity to human demonstrations in human-AI collaboration contexts.
Unlocking Advanced AI Capabilities for Enterprise
MIMIC offers a breakthrough in human-AI collaboration by enabling AI agents to understand and respond to human-like behaviors with unprecedented adaptability. Our framework significantly enhances key performance indicators across diverse enterprise applications.
2.5x
Behavior Diversity
90%
Fidelity to Human Demos
+30%
Coordination Success Rate
Deep Analysis & Enterprise Applications
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Inner Speech as a Mediational Mechanism
Steerable Behavior Generation
Vision-Language Model Scaffolding
Robustness and Adaptability
Inner Speech as a Mediational Mechanism
MIMIC conceptualizes inner speech as an internal linguistic representation that mediates between environmental perception and action selection. This framework, inspired by cognitive science, allows for diverse behavioral responses to identical stimuli, reflecting intrinsic motivations.
Steerable Behavior Generation
The framework enables fine-grained steering of behavior at inference time by conditioning the agent on behavior-specific speech. This allows for controlled generation of novel behaviors through designer-specified control, moving beyond goal-conditional generation.
Vision-Language Model Scaffolding
MIMIC leverages pre-trained vision-language models (VLMs) to provide external linguistic scaffolding. This generates descriptive characterizations of demonstrated behaviors, serving as training targets for a CVAE to internalize linguistic structure without explicit human annotation.
Robustness and Adaptability
Experiments across robotic manipulation tasks and human-AI collaboration games demonstrate that MIMIC significantly enhances both behavior diversity and fidelity. It enables nuanced behavioral steering without additional demonstrations and achieves higher cooperative rewards in multi-agent settings.
Enterprise Process Flow
Human Demonstrations
→
Inner Speech Generation (CVAE)
→
Behavior Cloning Policy (DDPM-T)
→
Agent Actions
80.21%
MIMIC Success Rate (Aligning Task)
| Approach | Behavioral Diversity | Designer Control | Language Grounding |
|---|---|---|---|
| Behavior Transformer |
|
No | No |
| Diffusion BC |
|
No | No |
| MIMIC (Ours) |
|
Full support | Yes |
Case Study: Overcooked Collaboration
In the Overcooked environment, MIMIC agents achieved significantly higher cooperative rewards than traditional BC approaches, demonstrating a +30% increase in collective reward. This highlights MIMIC's ability to anticipate and respond to human-like behaviors, making it an effective in silico human surrogate for pre-deployment testing and validation.
Project Your AI Impact: ROI Calculator
Estimate the potential efficiency gains and cost savings MIMIC can bring to your enterprise operations by automating complex human-AI coordination tasks.
Potential Annual Savings
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Your Implementation Roadmap
A structured approach to integrating MIMIC into your existing enterprise infrastructure, ensuring a seamless and successful deployment.
Phase 1: Data Ingestion & VLM Scaffolding
Collect human demonstrations and leverage pre-trained Vision-Language Models to generate initial linguistic descriptions for diverse behaviors, establishing the foundational training data for inner speech.
Phase 2: Inner Speech & Policy Training
Train the Conditional Variational Autoencoder (CVAE) for inner speech generation and the Diffusion-based Behavior Cloning policy, operationalizing the mediational mechanism between perception and action.
Phase 3: Steerable Behavior & Fine-tuning
Implement periodic inner speech generation and condition agent actions on this internal representation, enabling fine-grained, designer-specified control and adapting to changing contexts without additional demonstrations.
Phase 4: Multi-Agent Deployment & Validation
Integrate MIMIC agents into collaborative human-AI environments (e.g., Overcooked) and validate enhanced coordination, behavioral diversity, and fidelity to human expectations for robust enterprise deployment.
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