Position: A Call for Embodied AI
Authors: Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose Embodied AI (E-AI) as the next fundamental step in the pursuit of Artificial General Intelligence (AGI), juxtaposing it against current AI advancements, particularly Large Language Models (LLMs). We traverse the evolution of the embodiment concept across diverse fields (philosophy, psychology, neuroscience, and robotics) to highlight how E-AI distinguishes itself from the classical paradigm of static learning. By broadening the scope of E-AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston s active inference principle, offering a comprehensive approach to E-AI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future E-AI research. |
| Researcher Affiliation | Industry | 1Noah s Ark Lab, Huawei Technologies France, Paris, France 2London Research Center, London, UK. Correspondence to: Giuseppe Paolo <giuseppe.paolo@huawei.com>. |
| Pseudocode | No | The paper is a theoretical position paper and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a theoretical position paper and does not describe any specific software implementation, thus no open-source code is provided. |
| Open Datasets | No | The paper is a theoretical position paper and does not conduct empirical studies or use datasets for training. |
| Dataset Splits | No | The paper is a theoretical position paper and does not provide details on training, validation, or test dataset splits as it does not conduct empirical studies. |
| Hardware Specification | No | The paper discusses hardware limitations as a challenge for Embodied AI and mentions specific units like 'Google s Tensor Processing Unit (TPU)' and 'Huawei s Ascend chip' as advancements in the field, but it does not specify any hardware used for its own research or experiments. |
| Software Dependencies | No | The paper is a theoretical position paper and does not describe any software dependencies with specific version numbers used for its own research or experiments. |
| Experiment Setup | No | The paper is a theoretical position paper and does not conduct experiments, therefore no experimental setup details or hyperparameters are provided. |