I-Perceive: A Foundation Model for Vision-Language Active Perception

arXiv:2603.00600 · cs.RO · Submitted 2026-02-28 · Read on arXiv

cs.RO

Submitted: 2026-02-28

Updated: 2026-09-16

Terminology

• Active Perception — 45× in this paper · explained in 2 episodes
• Foundation Model — 17× in this paper · explained in 3 episodes
• Grounding — 9× in this paper · explained in 3 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• Constraint Violation — 7× in this paper
• Foundation Models — 7× in this paper · explained in 7 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Vision-Language Model — 7× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Semantic Understanding — 5× in this paper · explained in 2 episodes
• Vision-Language Navigation — 5× in this paper
• World Model — 5× in this paper · explained in 7 episodes
• Zero-Shot Generalization — 5× in this paper · explained in 5 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Active Vision — 3× in this paper · explained in 1 episode
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• Multimodal Inputs — 2× in this paper · explained in 2 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Active Exploration — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• LLM judge — 1× in this paper · explained in 2 episodes
• Late Fusion — 1× in this paper · explained in 2 episodes
• Structural Reasoning — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• Qwen3-VL Technical Report
• SpatialDreamer: Incentivizing Spatial Reasoning via Active Mental Imagery
• NaVILA: Legged Robot Vision-Language-Action Model for Navigation
• Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop
• Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding
• SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
• An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics
• Scene Exploration by Vision-Language Models
• The Replica Dataset: A Digital Replica of Indoor Spaces
• Gemini: A Family of Highly Capable Multimodal Models
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Wan: Open and Advanced Large-Scale Video Generative Models
• NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
• Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
• Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics
• ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning
• ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving