ASENA: Self-evolving Agents for Embodied Navigation

arXiv:2609.39207 · cs.RO · Submitted 2026-09-30 · Read on arXiv

cs.RO

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/foxglove/mcap

Project page: https://asena-bot.github.io

Terminology

• Self-Evolving Agents — 19× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Ablation — 2× in this paper · explained in 1 episode
• Active Exploration — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Action Translation — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• ASPIRE: Agentic /Skills Discovery for Robotics
• ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
• Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
• Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
• Memory-Centric Embodied Question Answering
• \textsc{NaVIDA}: Vision-Language Navigation with Inverse Dynamics Augmentation
• Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
• RynnBrain: Open Embodied Foundation Models
• Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation
• Qwen3-VL Technical Report
• SysNav: Multi-Level Systematic Cooperation Enables Real-World, Cross-Embodiment Object Navigation

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