POINav-Bench provides the first high-fidelity real-world benchmark for POI-goal VLN using 3DGS reconstructions of 126k m² with 163 POIs, supported by a Brain-Action framework and 70K real signage-entrance dataset.
Vlnverse: A benchmark for vision-language navigation with versatile, embodied, realistic simulation and evaluation
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IntentionNav is a new benchmark showing that VLMs infer intended targets from implicit instructions in 48% of cases but achieve only 25% terminal success and 5.5% grounded success in active navigation.
OmniNavBench is a unified benchmark for general-purpose navigation featuring composite multi-skill instructions, support for humanoid, quadrupedal and wheeled robots, and 1779 human teleoperated trajectories across 170 environments.
A feed-forward feature-Gaussian plus one-step geometry-aware pixel-flow simulator converts large image collections into 20K interactive scenes and 10M+ navigation samples that improve zero-shot Habitat and real-robot performance.
GroundControl estimates navigation uncertainty via statistical deviation from nominal goal-directed distance dynamics, achieving low E-AURC (0.0024 weighted for GPT-4o) on EB-Navigation splits and outperforming entropy and conformal baselines under SRCN evaluation.
Qwen-RobotNav provides a parameterized navigation model trained on 15.6M samples with vision-language co-training that achieves SOTA results on benchmarks and zero-shot transfer to real robots.
Proposes cost-aware question selection for ambiguous object navigation via information-gain analysis on corpora, a cost-penalizing benchmark, and a zero-shot MLLM agent.
A survey reviewing the architecture, usage patterns, and limitations of NVIDIA Isaac Sim across robotics domains.
citing papers explorer
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POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation
POINav-Bench provides the first high-fidelity real-world benchmark for POI-goal VLN using 3DGS reconstructions of 126k m² with 163 POIs, supported by a Brain-Action framework and 70K real signage-entrance dataset.
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IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction
IntentionNav is a new benchmark showing that VLMs infer intended targets from implicit instructions in 48% of cases but achieve only 25% terminal success and 5.5% grounded success in active navigation.
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Beyond Isolation: A Unified Benchmark for General-Purpose Navigation
OmniNavBench is a unified benchmark for general-purpose navigation featuring composite multi-skill instructions, support for humanoid, quadrupedal and wheeled robots, and 1779 human teleoperated trajectories across 170 environments.
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Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator
A feed-forward feature-Gaussian plus one-step geometry-aware pixel-flow simulator converts large image collections into 20K interactive scenes and 10M+ navigation samples that improve zero-shot Habitat and real-robot performance.
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GroundControl: Anticipating Navigation Failures in Vision-Language Agents via Trajectory-Consistent Uncertainty Estimates
GroundControl estimates navigation uncertainty via statistical deviation from nominal goal-directed distance dynamics, achieving low E-AURC (0.0024 weighted for GPT-4o) on EB-Navigation splits and outperforming entropy and conformal baselines under SRCN evaluation.
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Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Qwen-RobotNav provides a parameterized navigation model trained on 15.6M samples with vision-language co-training that achieves SOTA results on benchmarks and zero-shot transfer to real robots.
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Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation
Proposes cost-aware question selection for ambiguous object navigation via information-gain analysis on corpora, a cost-penalizing benchmark, and a zero-shot MLLM agent.
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NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics
A survey reviewing the architecture, usage patterns, and limitations of NVIDIA Isaac Sim across robotics domains.