Method-seeded agent architecture search yields confirmed or directional success-rate gains on four embodied executors, while exposing rollout noise, local basins, and partial credit assignment as hard constraints.
Explore until confident: Efficient exploration for embodied question answering
8 Pith papers cite this work. Polarity classification is still indexing.
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A VLM-based method for selecting exploration frontiers in robotics achieves up to 24% better map coverage than standard geometric heuristics in simulated indoor environments.
SAGE trains agents in physics-grounded semantic abstractions via RL with asymmetric clipping, achieving 53.21% LLM-Match Success on A-EQA (+9.7% over baseline) and encouraging physical robot transfer.
ObsGraph is a hierarchical observation-centric scene graph that unifies representation, retrieval, and multi-scale exploration for embodied reasoning.
Introduces EQA-Decision dataset with 4M+ QA pairs across four embodied reasoning dimensions and RoboDecision baseline for joint perception-reasoning-decision evaluation.
CORE Planner fuses sparse visibility graphs and Transformer contextual memory in RL for unknown-environment robot navigation, claiming 13-48% shorter paths and zero-shot sim-to-real transfer.
A survey that formalizes 3D Scene Graphs under a common definition, analyzes modeling choices, reviews construction from sensory data, examines applications and evaluations, and highlights open challenges with a supporting website.
citing papers explorer
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Automating the Design of Embodied Agent Architectures
Method-seeded agent architecture search yields confirmed or directional success-rate gains on four embodied executors, while exposing rollout noise, local basins, and partial credit assignment as hard constraints.
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Autonomous Frontier-Based Exploration with VLM Guidance
A VLM-based method for selecting exploration frontiers in robotics achieves up to 24% better map coverage than standard geometric heuristics in simulated indoor environments.
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Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation
SAGE trains agents in physics-grounded semantic abstractions via RL with asymmetric clipping, achieving 53.21% LLM-Match Success on A-EQA (+9.7% over baseline) and encouraging physical robot transfer.
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ObsGraph: Hierarchical Observation Representation for Embodied Reasoning and Exploration
ObsGraph is a hierarchical observation-centric scene graph that unifies representation, retrieval, and multi-scale exploration for embodied reasoning.
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Extending Embodied Question Answering from Perception to Decision
Introduces EQA-Decision dataset with 4M+ QA pairs across four embodied reasoning dimensions and RoboDecision baseline for joint perception-reasoning-decision evaluation.
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CORE Planner: Contextual-memory Oriented Reinforcement-learning in Unknown Environments for Robot Navigation
CORE Planner fuses sparse visibility graphs and Transformer contextual memory in RL for unknown-environment robot navigation, claiming 13-48% shorter paths and zero-shot sim-to-real transfer.
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3D Scene Graphs: Open Challenges and Future Directions
A survey that formalizes 3D Scene Graphs under a common definition, analyzes modeling choices, reviews construction from sensory data, examines applications and evaluations, and highlights open challenges with a supporting website.
- RoboAtlas: Contextual Active SLAM