Object-Centric Scene Representations Using Active Inference
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an object-centric generative model for robot scene understanding. The active inference agent effectively balances exploration and goal-seeking, outperforming other methods in object viewpoint tasks.
Area Of Science
- Robotics
- Artificial Intelligence
- Neuroscience
Background
- Robots require scene understanding to interact with environments.
- Current methods struggle with inferring object properties from raw sensory data.
Purpose Of The Study
- To develop a novel approach for robot scene understanding using an object-centric generative model.
- To introduce a new benchmark for evaluating active vision agents in 3D object viewpoint tasks.
Main Methods
- Utilized an object-centric generative model for scene understanding.
- Employed active inference, a neuro-inspired framework, for perception and action.
- Developed a benchmark for active vision agents to find optimal object viewpoints in a 3D workspace.
Main Results
- The active inference agent successfully inferred object category and pose in an allocentric frame.
- The agent demonstrated a balance between epistemic foraging and goal-driven behavior.
- Achieved over double the success rate compared to supervised and reinforcement learning baselines.
Conclusions
- The proposed object-centric generative model enhances robot scene understanding capabilities.
- Active inference provides an effective framework for robot perception and action.
- The new benchmark facilitates rigorous evaluation of active vision agents.
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