Research map / Vision-language and multimodal / Robot action and navigation
Natural Language Acquisition and Grounding for Embodied Robotic Systems
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn grammar from unlabelled video-language data instead of supervised parsing | Learned PCFG · Synthetic novel command parsing | fully correct trees: 69.2% vs 90.7% | Hurt |
Closest papers by meaning
- Grounding Language Plans in Demonstrations Through Counterfactual Perturbations (ICLR 2024 · similarity 0.87)
- Learning with Language-Guided State Abstractions (ICLR 2024 · similarity 0.87)
- I-Perceive: A Foundation Model for Vision-Language Active Perception (NeurIPS 2026 · similarity 0.87)
- PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs (ICML 2024 · similarity 0.87)
- RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation (CVPR 2026 · similarity 0.87)
- RoboGround: Robotic Manipulation with Grounded Vision-Language Priors (CVPR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.