Spacemind A Modular And Self Evolving Embodied Vis

---
name: spacemind-a-modular-and-self-evolving-embodied-vis
description: 'Research paper: SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework '
metadata:
  source: arXiv
  arxiv_id: 2604.14399
  published: 2026-04-15
  utility_score: 1.0
  keywords: self-evolving, tool, tools, reasoning, robustness, autonomous, fine-tuning
---

# SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing

**arXiv ID:** 2604.14399  
**Published:** 2026-04-15  
**Utility Score:** 1.0  
**URL:** http://arxiv.org/abs/2604.14399

## Authors
Aodi Wu, Haodong Han, Xubo Luo

## Categories
cs.RO, cs.AI, eess.SY

## Abstract
Autonomous on-orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi-phase tasks over extended horizons. We present SpaceMind, a modular and self-evolving vision-language model (VLM) agent framework that decomposes knowledge, tools, and reasoning into three independently extensible dimensions: skill modules with dynamic routing, Model Context Protocol (MCP) tools with configurable profiles, and injectable reasoning-mode skills. An MCP-Redis interface layer enables the same codebase to operate across simulation and physical hardware without modification, and a Skill Self-Evolution mechanism distills operational experience into persistent skill files without model fine-tuning. We validate SpaceMind through 192 closed-loop runs across five satellites, three task types, and two environments, a UE5 simulation and a physical laboratory, deliberately including degraded conditions to stress-test robustness. Under nominal conditions all modes achieve 90--100% navigation success; under degradation, the Prospective mode uniquely succeeds in search-and-approach tasks where other modes fail. A self-evolution study shows that the agent recovers from failure in four of six groups from a single failed episode, including complete failure to 100% success and inspection scores improving from 12 to 59 out of 100. Real-world validation confirms zero-code-modification transfer to a physical robot with 100% rendezvous success. Code: https://github.com/wuaodi/SpaceMind

## Matched Keywords
self-evolving, tool, tools, reasoning, robustness, autonomous, fine-tuning

## Relevance to AI Agents
This paper is highly relevant to AI agent systems research with focus on:
- self-evolving, tool, tools, reasoning, robustness

## Quick Reference
```bash
# View paper
open http://arxiv.org/abs/2604.14399

# Download PDF
open http://arxiv.org/pdf/2604.14399.pdf
```

---
*Auto-generated from arXiv on 2026-04-17*

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## Tools Used

- `Read` - Read existing files and documentation
- `Write` - Create new files and documentation
- `Bash` - Execute commands when needed

## Instructions for Agents

1. Identify user's intent and specific requirements
2. Gather necessary context from files or user input
3. Execute appropriate actions using available tools
4. Provide clear results and suggest next steps

## Examples

### Basic Spacemind A Modular And Self Evolving Embodied Vis usage
```
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→ Understand requirements → Execute actions → Provide results
```

### Advanced usage
```
User: "I need detailed spacemind a modular and self evolving embodied vis assistance"
→ Clarify scope → Provide comprehensive solution → Follow up
```