RoboLab is a new simulation benchmark with 120 tasks across visual, procedural, and relational axes that quantifies generalization gaps and perturbation sensitivity in task-generalist robotic policies.
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PaliGemma: A versatile 3B VLM for transfer
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PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.
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- abstract PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.
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VLA language backbones show high redundancy on manipulation benchmarks, with half the LLM blocks removable and even two blocks sufficient to recover baseline performance after fine-tuning, unlike vision and action pathways.
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AT-VLA proposes adaptive tactile injection and a dual-stream tactile reaction mechanism to enhance VLA models for contact-rich robotic manipulation with real-time responses.
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Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding
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mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
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SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding
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Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
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GEM: Generative Supervision Helps Embodied Intelligence
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OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation
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TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation
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LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model
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Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice
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Exploring and Exploiting Stability in Latent Flow Matching
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VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
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The Amazing Stability of Flow Matching
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Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference
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Attention Grounded Enhancement for Visual Document Retrieval
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XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
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