In the intricate landscape of human cognition, language is not merely a bridge for communication but a fundamental instrument for thought. This profound connection between language and cognitive processes, illuminated through internal dialogues or "inner speech," underlines the essential role of language in self-reflection and in-depth contemplation. Recently, LLMs have shown remarkable capability in processing and generating human-like language, showcasing potential in mimicking human language processing. However, reaching or surpassing true human-level intelligence is still challenging. These include understanding concepts of time and space, recognizing self-capabilities, engaging in self-reflection and progress, exhibiting subjective agency, continuous learning, and even the development of self-awareness. Powered by these models, LLM agents have been developed with perception and action abilities to deal with more complex tasks. Similar to how humans and other living organisms interact with environment and achieve self-growth, endowing LLMs with the capabilities to interact with environments could be a pathway toward realizing AGI or even SLA (silicon-based life agents). Traditional robotics has achieved remarkable achievements by mimicking this kind of biological mechanisms, offering valuable insights for the evolution of LLM agents. This paper proposes a paradigm shift, distinct from applying LLMs to robotics. It focuses on integrating essential robotics principles into LLM agents, such as sensory perception, planning for dynamic environmental interaction, and real-time adaptive feedback, into the framework of LLM agents. This integration aims to extend the capabilities of LLMs beyond linguistic proficiency, endowing them with a more nuanced understanding of real-world contexts like time, space, energy to enhance their decision-making processes, enable action capabilities, realize self-reflection and growth, pushing LLM agents towards a more comprehensive emulation, and potentially surpassing, human intelligence. However, the transition to silicon-based intelligent entities is not just a technological challenge but also involves profound philosophical and ethical considerations. The development of AGI might redefine the essence and boundaries of intelligence, necessitating cautious contemplation of the technological advancements’ impacts on society and human values. This interdisciplinary integration aims to advance AGI research while addressing the potential social, ethical, and technical challenges in this evolving field.
Keywords: LFM, agent, robotics, silicon-based life, agi, rewawrd in thought
Figure 1: Robotics enable Large foundation model: a path towards silicon-based life intelligent robot.
Figure 2: The reason why a silicon-based intelligence agent or robot is considered a living entity
In the next 10 years, we could develop superintelligence which is far smarter than humans\cite{burns2023weak}, and along with it, silicon-based life intelligence agents or robots may also emerge.
At the heart of human cognition and interaction lies the profound and intricate ability of language. It's not merely a tool for communication; it serves as a fundamental medium through which human thinking is realized and articulated. For instance, as intelligent agents, humans employ language as the primary mechanism for processing thoughts. This deep intertwining of language and cognitive processes underscores the criticality of replicating linguistic capabilities in the realm of artificial intelligence.
The evolution of Large Language Models (LLMs) represents a significant stride in this pursuit. These models have transitioned from their early stages to their current form, mirroring the relentless quest for advanced AI capabilities. However, their journey is marked by inherent limitations. Present-day LLMs, despite their impressive linguistic abilities, encounter constraints like fixed-length context windows and challenges in sustaining long-term memory. Crucially, as language models, they lack a temporal dimension, the ability for self-reflection and improvement, and both short-term and long-term learning capacities. These shortcomings spotlight the daunting hurdles that must be surmounted to advance from sophisticated LLMs towards the ultimate goal of Artificial General Intelligence (AGI), an ambition still shrouded in complexity.
Simultaneously, there has been remarkable progress in developing agents powered by LLMs. These agents are venturing into more complex functionalities such as autonomous driving, robotic grasping, autonomous programming tasks, and sociological studies in multi-agent systems. Typically, these agents are structured with four integral modules: profiling, memory, planning, and action. The profiling module identifies the agent's role, while the memory and planning modules position the agent within dynamic environments, enabling recollection of past behaviors and planning future actions. The action module translates the agent's decisions into specific outputs. In this framework, the profiling module influences the memory and planning modules, which in turn collectively impact the action module. By integrating advanced language understanding and generation capabilities, these agents are setting the stage for more intuitive and versatile AI applications, marking a crucial convergence of linguistic intelligence and autonomous decision-making. While LLM agents represent a significant advancement in AI, they are still a considerable distance from achieving the level of autonomy, understanding, and adaptability inherent in AGI. The journey towards AGI requires not only advancements in language processing and decision-making but also significant improvements in contextual understanding, memory retention, complex problem-solving, and sensory integration.
However, insights from the field of robotics may could provide a broader context for this evolution. Robotics, with its foundational elements of sensing, control, and planning, offers invaluable perspectives for integrating AI, especially in the form of LLMs and AI agents, into physical and interactive environments. The multifaceted aspects of robotics, including multi-sensor information, multi-input multi-output controllers, feedback control theories, PID controllers, control cycles, reinforcement learning theories, and energy management strategies, can offer novel approaches to evolve LLMs towards autonomous agents, or even life-like entities. For instance, the parallels between multi-sensor integration in robotics and multimodal capabilities in agents, multi-input multi-output controllers and multi-tasking operations in agents, feedback control theories and self-reflective behaviors in multi-agent systems, and the temporal concepts in PID controllers related to the agents' understanding of time, all suggest potential technical pathways. Additionally, the parallels between the reinforcement learning's reward mechanisms and agents' reward systems, as well as energy management in robots compared to energy utilization concepts in agents (akin to human instincts like hunger), provide intriguing technical analogies.
In the following sections, we will explore how knowledge from the field of robotics can be leveraged to advance LLMs towards the development of intelligent life-like entities.
Traditional theory: Filter, Feature extraction, Fusing, Signal processing, FFT, CV, etc.
Static date, time-series data, spacetime data and related algorithms.
How to make agents understand the time and space data?
Multimodal: VLM, LMM, MLLMs.
Helpful for decision making (Perceive self-limitations, energy/CPU/GPU, temperature, etc.)
What is the minimum number of types of data required to realize AGI/SLIR?
Can sensor data other than language be described using language?
How to processing two or more kinds of data at the same time? (Language + others).
Feedback control: How can an agent achieve self-reflection?
Realtime control: Does agents need have the concept of time?
Realtime control: How to ensure reaction time like human(200ms)?
Control loop: How to make agents to realize continuous thinking?
Disturbance in control loop: How to integrate new information into ongoing thought?
Reinforcement learning: Reward/critic in Thought is helpful for self-awareness?
Can Reward in Thought represent as a certain level of dopamine mechanism?
Reinforcement learning: How to realize self-improvement, lifelong learning?
MIMO controller: How to make agents have the ability of dealing with multitask?
Global planning: Long-Term Goal Setting and Strategy Formulation.
Global planning: Complex Task Decomposition and Management.
Global planning: Multi-task Handling and Priority Assessment.
Dynamic planning: Step-by-Step Decision Optimization, Learning and Feedback.
Dynamic planning: Resource Optimization and Adaptability.
How to use traditional planning algorithm like A*, Dijkstra, dynamic planning, RRT, RRT* etc, to improve the CoT, ToT, GoT, CoC, etc?
Reinforcement learning: Reward in Thought, online self-improvement, online-self-reflection.
bibtex
@misc{universea-rototicsllm,
title = {Large Language Models Meet Robotics: A Path Towards Silicon-based Life Intelligence},
author = {Pengsong Zhang},
year = {2024},
howpublished = {GitHub},
url = {https://github.com/universea/Robotics-Meet-Foundation-Models-Perspective}
}