A hybrid semantic graph and retrieval-augmented system with parameter-efficient VLMs achieves state-of-the-art inference and querying speeds on embodied navigation tasks with competitive accuracy.
Meta -Memory: Retrieving and Integrating Semantic - Spatial Memories for Robot Spatial Reasoning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.RO 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
BrainMem equips LLM-based embodied planners with working, episodic, and semantic memory that evolves interaction histories into retrievable knowledge graphs and guidelines, raising success rates on long-horizon 3D benchmarks.
The Semantic Autonomy Stack combines a seven-step parametric resolver handling 88% of instructions in under 0.1 ms with VLM escalation and a five-category cross-robot memory system, achieving 100% accuracy and 103,000-fold latency reduction on Raspberry Pi 5 robots with no GPU or training data.
A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.
citing papers explorer
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EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents
A hybrid semantic graph and retrieval-augmented system with parameter-efficient VLMs achieves state-of-the-art inference and querying speeds on embodied navigation tasks with competitive accuracy.
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BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning
BrainMem equips LLM-based embodied planners with working, episodic, and semantic memory that evolves interaction histories into retrievable knowledge graphs and guidelines, raising success rates on long-horizon 3D benchmarks.
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A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory
The Semantic Autonomy Stack combines a seven-step parametric resolver handling 88% of instructions in under 0.1 ms with VLM escalation and a five-category cross-robot memory system, achieving 100% accuracy and 103,000-fold latency reduction on Raspberry Pi 5 robots with no GPU or training data.
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Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.