Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction

arXiv:2609.28467 · cs.RO, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Grounding — 32× in this paper · explained in 3 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Clustering — 4× in this paper · explained in 2 episodes
• In-Context Learning — 4× in this paper · explained in 4 episodes
• Spectral clustering — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Action Primitives — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
• OmniVLA: An Omni-Modal Vision-Language-Action Model for Robot Navigation
• RTMW: Real-Time Multi-Person 2D and 3D Whole-body Pose Estimation

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