Deploying Foundation Models for Embodied Navigation

arXiv:2609.25666 · cs.RO · Submitted 2026-09-22 · Read on arXiv

cs.RO

Submitted: 2026-09-22

Updated: 2026-09-22

Terminology

• Foundation Model — 10× in this paper · explained in 3 episodes
• Foundation Models — 9× in this paper · explained in 7 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• Embodied AI — 4× in this paper · explained in 2 episodes
• Conference on Robot Learning — 3× in this paper
• Foundation Models (FMs) — 3× in this paper · explained in 2 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Affordance — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Dynamic Object Map — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Imitation Learning — 1× in this paper
• Imitation Learning (IL) — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Memory Augmentation — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Transit-Aware Planning — 1× in this paper

Sources

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Rearrangement: A Challenge for Embodied AI
• ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
• Scaling Laws for Neural Language Models
• Instance-Specific Image Goal Navigation: Training Embodied Agents to Find Object Instances
• Self-Supervised Object Goal Navigation with In-Situ Finetuning
• Open-World Object Manipulation using Pre-trained Vision-Language Models
• FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation
• EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

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