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Self-evolving Embodied AI

Tongtong Feng, Xin Wang, Wenwu Zhu

TL;DR

This paper addresses the limitations of fixed-memory, fixed-embodiment embodied AI in open-world settings by introducing self-evolving embodied AI. It proposes a unified framework with five co-evolving modules—memory self-updating, task self-switching, environment self-prediction, embodiment self-adaptation, and model self-evolution—driven by the agent's state and interactions, forming a closed-loop of continual adaptation. The authors survey state-of-the-art methods for each component, discuss practical applications in robotics, driving, and UAVs, and outline future directions for controllable, trustworthy, and swarm self-evolution. The work provides a pathway toward continually adaptive intelligence and offers a new perspective on achieving general artificial intelligence in dynamic environments.

Abstract

Embodied Artificial Intelligence (AI) is an intelligent system formed by agents and their environment through active perception, embodied cognition, and action interaction. Existing embodied AI remains confined to human-crafted setting, in which agents are trained on given memory and construct models for given tasks, enabling fixed embodiments to interact with relatively static environments. Such methods fail in in-the-wild setting characterized by variable embodiments and dynamic open environments. This paper introduces self-evolving embodied AI, a new paradigm in which agents operate based on their changing state and environment with memory self-updating, task self-switching, environment self-prediction, embodiment self-adaptation, and model self-evolution, aiming to achieve continually adaptive intelligence with autonomous evolution. Specifically, we present the definition, framework, components, and mechanisms of self-evolving embodied AI, systematically review state-of-the-art works for realized components, discuss practical applications, and point out future research directions. We believe that self-evolving embodied AI enables agents to autonomously learn and interact with environments in a human-like manner and provide a new perspective toward general artificial intelligence.

Self-evolving Embodied AI

TL;DR

This paper addresses the limitations of fixed-memory, fixed-embodiment embodied AI in open-world settings by introducing self-evolving embodied AI. It proposes a unified framework with five co-evolving modules—memory self-updating, task self-switching, environment self-prediction, embodiment self-adaptation, and model self-evolution—driven by the agent's state and interactions, forming a closed-loop of continual adaptation. The authors survey state-of-the-art methods for each component, discuss practical applications in robotics, driving, and UAVs, and outline future directions for controllable, trustworthy, and swarm self-evolution. The work provides a pathway toward continually adaptive intelligence and offers a new perspective on achieving general artificial intelligence in dynamic environments.

Abstract

Embodied Artificial Intelligence (AI) is an intelligent system formed by agents and their environment through active perception, embodied cognition, and action interaction. Existing embodied AI remains confined to human-crafted setting, in which agents are trained on given memory and construct models for given tasks, enabling fixed embodiments to interact with relatively static environments. Such methods fail in in-the-wild setting characterized by variable embodiments and dynamic open environments. This paper introduces self-evolving embodied AI, a new paradigm in which agents operate based on their changing state and environment with memory self-updating, task self-switching, environment self-prediction, embodiment self-adaptation, and model self-evolution, aiming to achieve continually adaptive intelligence with autonomous evolution. Specifically, we present the definition, framework, components, and mechanisms of self-evolving embodied AI, systematically review state-of-the-art works for realized components, discuss practical applications, and point out future research directions. We believe that self-evolving embodied AI enables agents to autonomously learn and interact with environments in a human-like manner and provide a new perspective toward general artificial intelligence.
Paper Structure (39 sections, 1 figure, 1 table)

This paper contains 39 sections, 1 figure, 1 table.

Table of Contents

  1. 1. Introduction
  2. 2. Self-evolving Embodied AI
  3. 2.1 The Definition and Framework
  4. Framework
  5. 2.2 The Components
  6. Memory self-updating
  7. Task self-switching
  8. Environment self-prediction
  9. Embodiment self-adaptation
  10. Model self-evolution
  11. 2.3 Self-evolving Mechanisms
  12. 3. Methodologies
  13. 3.1 Memory self-updating
  14. Memory self-editing
  15. Memory self-organization
  16. ...and 24 more sections

Figures (1)

  • Figure 1: Comparison between existing embodied AI and self-evolving embodied AI. (a) Existing embodied AI operates based on given task, given memory, given embodiment, and given environment to pretrain the corresponding model, which relies heavily on external human guidance and empirical configurations. (b) Self-evolving embodied AI operates based on its changing state and dynamic environment with memory self-updating, task self-switching, embodiment self-adaptation, environment self-prediction, and model self-evolution, aiming to achieve continually adaptive intelligence over time $t$ with autonomous evolution.

Theorems & Definitions (1)

  • Definition 1: Self-evolving embodied AI