受昆虫启发的视觉导航模型，用极低算力实现高效路径规划。
Insect-inspired Visual Point-goal Navigation
- 融合蘑菇体与中央复合体结构，模拟昆虫学习与路径记忆机制。
- 碰撞触发学习，路径优化成功率媲美顶尖模型，算力仅为其千分之一。
- 适合资源受限场景下的机器人自主导航，尤其看重效率的系统。
昆虫神经行为学为高效自主导航提供了理想的生物模板。本文将具身人工智能中的视觉点目标导航任务与昆虫在食物源与巢穴间发现、学习并优化绕障路径的能力相类比。我们构建了一个整合蘑菇体（mushroom body）与中央复合体（central complex）的新型模型，前者负责关联学习，后者负责路径积分。实验表明，由碰撞触发的蘑菇体学习能实现自适应避障，从而生成更优路径，验证了近期行为学研究中关于昆虫可边行进边持续学习的假说。该具身式昆虫启发模型在标准 Habitat 点目标导航基准测试中，以数个数量级更低的计算成本达到与最新先进模型相当的成功率。在更真实的仿真环境中测试也证明其对扰动具有鲁棒性。
原文摘要 · Abstract (English)
Insect neuroethology provides a compelling biological template for efficient autonomous navigation. We draw an analogy between the formal embodied AI visual point-goal navigation task and the ability of insects to discover, learn, and refine visually guided paths around obstacles between a discovered food location and their nest. We develop a novel integrative model of mushroom body and central complex, two insect brain structures, that have been implicated, respectively, in associative learning and path integration. We demonstrate the mushroom body learning triggered by collisions results in adaptive obstacle avoidance and consequently optimised paths to the goal, corroborating the hypothesis of recent behavioural work that an insect can learn continuously as they travel. The embodied insect-inspired model achieves success rates comparable to recent state-of-the-art models at many orders of magnitude less computational cost in the standardised Habitat point-goal navigation benchmark. Testing in a more realistic simulated environment validates its robustness to perturbations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。