EPIC-Bench is a new fine-grained benchmark that shows leading VLMs struggle with multi-target counting, part-whole relations, and affordance detection in real-world embodied visual grounding tasks.
InarXiv preprint arXiv:2311.00899
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RoboJailBench creates a taxonomy-based benchmark, intent-contrast datasets, and evaluation framework for jailbreak attacks and defenses in embodied robotic AI systems.
3D-VLA is a new embodied foundation model that uses a 3D LLM plus aligned diffusion models to generate future images and point clouds for improved reasoning and action planning in 3D environments.
RT-H learns robot policies by first predicting language motions as an intermediate representation and then mapping those plus the high-level task to actions, yielding more robust multi-task performance and the ability to learn from language interventions.
MolmoAct2 is an open VLA model that outperforms baselines like Pi-05 on 7 benchmarks and whose backbone surpasses GPT-5 on 13 embodied-reasoning tasks through new datasets, specialized training, and architecture changes for lower latency.
MiniVLA-Nav v1 provides 1,174 episodes of language-instructed robot navigation in photorealistic simulations with RGB, depth, segmentation, and expert action data.
XEmbodied is a foundation model that integrates 3D geometric and physical signals into VLMs using a 3D Adapter and Efficient Image-Embodied Adapter, plus progressive curriculum and RL post-training, to improve spatial reasoning and embodied performance on 18 benchmarks.
citing papers explorer
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EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
EPIC-Bench is a new fine-grained benchmark that shows leading VLMs struggle with multi-target counting, part-whole relations, and affordance detection in real-world embodied visual grounding tasks.
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RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents
RoboJailBench creates a taxonomy-based benchmark, intent-contrast datasets, and evaluation framework for jailbreak attacks and defenses in embodied robotic AI systems.
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3D-VLA: A 3D Vision-Language-Action Generative World Model
3D-VLA is a new embodied foundation model that uses a 3D LLM plus aligned diffusion models to generate future images and point clouds for improved reasoning and action planning in 3D environments.
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RT-H: Action Hierarchies Using Language
RT-H learns robot policies by first predicting language motions as an intermediate representation and then mapping those plus the high-level task to actions, yielding more robust multi-task performance and the ability to learn from language interventions.
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MolmoAct2: Action Reasoning Models for Real-world Deployment
MolmoAct2 is an open VLA model that outperforms baselines like Pi-05 on 7 benchmarks and whose backbone surpasses GPT-5 on 13 embodied-reasoning tasks through new datasets, specialized training, and architecture changes for lower latency.
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MiniVLA-Nav v1: A Multi-Scene Simulation Dataset for Language-Conditioned Robot Navigation
MiniVLA-Nav v1 provides 1,174 episodes of language-instructed robot navigation in photorealistic simulations with RGB, depth, segmentation, and expert action data.
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XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
XEmbodied is a foundation model that integrates 3D geometric and physical signals into VLMs using a 3D Adapter and Efficient Image-Embodied Adapter, plus progressive curriculum and RL post-training, to improve spatial reasoning and embodied performance on 18 benchmarks.