Experiments reveal that topological cues robustly support LLM navigation planning while incorrect semantic cues derail it, with linguistic format effects varying by model size and compression.
Neural slam: Learning to explore with external memory
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Tensor Memory augments Transformers with a constant-size 3D voxel grid using differentiable soft writes at predicted locations, local interaction, and gated recurrent dynamics to decouple memory capacity from sequence length.
A proof-of-concept UAV system integrates YOLOv2 detection, dual-stream navigation CNN, and InceptionV3 LRCN to achieve error-free identification of 17 individual cattle over 146.7 minutes of autonomous low-altitude flight.
Multi-step predictive generative models form stable belief states capturing environment layout and agent pose, yielding higher data efficiency on RL tasks than model-free agents.
Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.
citing papers explorer
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The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
Experiments reveal that topological cues robustly support LLM navigation planning while incorrect semantic cues derail it, with linguistic format effects varying by model size and compression.
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Tensor Memory: Fixed-Size Recurrent State for Long-Horizon Transformers
Tensor Memory augments Transformers with a constant-size 3D voxel grid using differentiable soft writes at predicted locations, local interaction, and gated recurrent dynamics to decouple memory capacity from sequence length.
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
A proof-of-concept UAV system integrates YOLOv2 detection, dual-stream navigation CNN, and InceptionV3 LRCN to achieve error-free identification of 17 individual cattle over 146.7 minutes of autonomous low-altitude flight.
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Shaping Belief States with Generative Environment Models for RL
Multi-step predictive generative models form stable belief states capturing environment layout and agent pose, yielding higher data efficiency on RL tasks than model-free agents.
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To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments
Classical agents outperform learning-based ones on MINOS and Stanford 3D Indoor Spaces, with learned agents weaker at collision avoidance and memory but stronger at handling ambiguity and noise.