End-to-end Listen, Look, Speak and Act
Abstract
Human interaction is inherently multimodal and full-duplex: we listen while watching, speak while acting, and fluidly adapt to turn-taking and interruptions. Realizing these capabilities is essential for building models simulating humans. We present ELLSA (End-to-end Listen, Look, Speak and Act), which, to our knowledge, is the first full-duplex, end-to-end model that simultaneously perceives and generates across vision, text, speech, and action within a single architecture, enabling interaction patterns previously out of reach, yielding more natural, human-like behaviors. At its core is a novel SA-MoE architecture (Self-Attention Mixture-of-Experts) that routes each modality to specialized experts and fuses them through a unified attention backbone. This provides a generalizable solution for joint multimodal perception and concurrent generation, leveraging strong pre-trained components while enabling efficient modality integration and mitigating modality interference. On speech-interaction and robot-manipulation benchmarks, ELLSA matches modality-specific baselines, while uniquely supporting advanced multimodal and full-duplex behaviors such as dialogue and action turn-taking, defective instruction rejection, speaking-while-acting, context-grounded visual question answering, and action barge-ins. We contend that ELLSA represents a step toward more natural and general interactive intelligence, contributing to the broader pursuit of artificial general intelligence. All data, code and model checkpoints will be released upon acceptance.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents ELLSA, a pioneering multimodal architecture that enables full-duplex interaction across various modalities, representing a substantial step towards more natural human-like AI systems. The innovative methodology and promising experimental results position this work as a significant contribution to the field of machine learning and artificial intelligence.
Methodology
The paper introduces ELLSA, an innovative architecture that integrates multiple modalities (vision, text, speech, and action) in a full-duplex manner. The use of a Self-Attention Mixture-of-Experts (SA-MoE) architecture is particularly noteworthy, as it allows for specialized processing of each modality while maintaining a unified attention mechanism. This approach effectively addresses the challenges of modality interference and integration, making it a significant advancement in multimodal interaction models.
Results & evaluation
The experiments conducted on speech-interaction and robot-manipulation benchmarks demonstrate that ELLSA performs comparably to modality-specific baselines. The evaluation of advanced multimodal behaviors, such as dialogue turn-taking and context-grounded visual question answering, showcases the model's capabilities in real-world scenarios. However, the paper could benefit from more extensive comparisons with existing state-of-the-art models to better contextualize its performance.
Reproducibility
The authors commit to releasing all data, code, and model checkpoints upon acceptance, which is a positive step towards ensuring reproducibility. However, the paper lacks detailed implementation specifics that would aid in replicating the results independently.
Limitations
One limitation is the potential complexity and computational cost associated with the SA-MoE architecture, which may hinder its scalability in real-time applications. Additionally, the paper does not address the robustness of the model in noisy or unpredictable environments, which is critical for practical deployment.
Broader impact
ELLSA's ability to simulate human-like interactions has significant implications for the development of more intuitive AI systems in various domains, including robotics, virtual assistants, and interactive gaming. By advancing the state of multimodal interaction, this work contributes to the broader pursuit of artificial general intelligence. The paper presents ELLSA, a pioneering multimodal architecture that enables full-duplex interaction across various modalities, representing a substantial step towards more natural human-like AI systems. The innovative methodology and promising experimental results position this work as a significant contribution to the field of machine learning and artificial intelligence.
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