Arjun P S

Arjun P S, Andrew Melnik, Gora Chand Nandi

Experience Goal Visual Rearrangement task stands as a foundational challenge within Embodied AI, requiring an agent to construct a robust world model that accurately captures the goal state. The agent uses this world model to restore a shuffled scene to its original configuration, making an accurate representation of the world essential for successfully completing the task. In this work, we present a novel framework that leverages on 3D Gaussian Splatting as a 3D scene representation for experience goal visual rearrangement task. Recent advances in volumetric scene representation like 3D Gaussian Splatting, offer fast rendering of high quality and photo-realistic novel views. Our approach enables the agent to have consistent views of the current and the goal setting of the rearrangement task, which enables the agent to directly compare the goal state and the shuffled state of the world in image space. To compare these views, we propose to use a dense feature matching method with visual features extracted from a foundation model, leveraging its advantages of a more universal feature representation, which facilitates robustness, and generalization. We validate our approach on the AI2-THOR rearrangement challenge benchmark and demonstrate improvements over the current state of the art methods

Arjun P S, Andrew Melnik, Gora Chand Nandi

Recent advancements in Generative AI, particularly in Large Language Models (LLMs) and Large Vision-Language Models (LVLMs), offer new possibilities for integrating cognitive planning into robotic systems. In this work, we present a novel framework for solving the object goal navigation problem that generates efficient exploration strategies. Our approach enables a robot to navigate unfamiliar environments by leveraging LLMs and LVLMs to understand the semantic structure of the scene. To address the challenge of representing complex environments without overwhelming the system, we propose a 3D modular scene representation, enriched with semantic descriptions. This representation is dynamically pruned using an LLM-based mechanism, which filters irrelevant information and focuses on task-specific data. By combining these elements, our system generates high-level sub-goals that guide the exploration of the robot toward the target object. We validate our approach in simulated environments, demonstrating its ability to enhance object search efficiency while maintaining scalability in complex settings.