RobotValues is a benchmark of 10K value-conflict scenarios that reveals VLMs default to safety and accommodation while failing to follow instructions to prioritize other values 80% of the time.
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
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abstract
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website https://robotics-transformer-x.github.io.
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- abstract Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and enviro
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In a two-period game-theoretic model of learning-by-deploying, data pooling raises welfare with fixed prices but can turn privately unprofitable under Cournot competition, with a sustainability threshold set by demand elasticity.
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.
REGEN uses recurrent generative replays from World Action Models to cut catastrophic forgetting by up to 50% in continual imitation learning compared to sequential fine-tuning.
Masking the end-effector from wrist views during training lets a single-gripper VLA transfer zero-shot to other grippers, arms, and five-fingered hands while keeping original performance.
Processed egocentric human video outperforms teleoperated real-robot trajectories as pretraining data for embodied foundation models, delivering 24% lower validation loss and 52.5-90% higher task success rates under matched post-training protocols.
EgoCS-400K is a new 400K-video egocentric CS dataset with action-state-event alignment from public match demos for world model training.
ThinkingVLA is a Mixture-of-Transformers VLA model that performs interleaved forward CoT for subgoal and image prediction followed by inverse CoT grounded on the predicted image to generate actions.
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
UMI-Bench 1.0 is presented as the first open benchmark dedicated to reproducible real-world evaluation of Universal Manipulation Interface policies.
World models introduce a stealthy poisoning vector into robot learning pipelines where malicious prompts or dynamics in teleoperated data activate only during synthetic trajectory generation, enabling backdoors in downstream policies.
ActionMap introduces a voxel heatmap action head for VLA models that improves policy learning by exploiting geometric structure in the action space.
Action-only curation metrics for imitation learning fail to detect structural defects that degrade policies, while state-aware metrics recover roughly one-third of the performance gap.
DVAC uses denoising variance as an intrinsic signal to adaptively chunk actions in flow-based robot policies, improving success rates and cutting replans on LIBERO, RoboTwin, CALVIN, and real-world tasks.
The paper identifies a deployment safety gap in VLA policies where identical checkpoints can be executable-inequivalent due to action metadata mismatches, supported by a derived closed-form transform and empirical drift measurements on LIBERO benchmarks.
TTT-VLA performs test-time training for VLA models by optimizing only a latent prompt on new interaction data via a proxy self-supervised signal, yielding higher task success rates on SimplerEnv in single- and multi-embodiment settings.
BOKBO is the first conformal abstention method for K-sample VLA policies that supplies finite-sample distribution-free guarantees on executed violation rates, with global and Mondrian per-task variants.
PhAIL provides an open benchmark and distributional evaluation method for real-robot VLA policies using time-to-success CDF, HRT scoring, and KS significance tests.
SkiP introduces action relabeling and Motion Spectrum Keying to skip redundant steps in robot trajectories, cutting executed steps by 15-40% while maintaining success rates across 72 simulated and 3 real tasks.
Flow map policies enable fast one-step inference for flow-based RL policies, and FMQ provides an optimal closed-form Q-guided target for offline-to-online adaptation under trust-region constraints, achieving SOTA performance.
A3 reframes dynamic action chunk commitment in VLA models as self-speculative prefix verification, accepting the longest continuous sequence of actions that satisfies consensus-ordered conditional invariance and prefix-closed sequential consistency.
SABER provides 44.8K multi-representation action samples from unscripted retail environments that raise a VLA model's mean success rate on ten manipulation tasks from 13.4% to 29.3%.
OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
Action Agent pairs LLM-driven video generation with a flow-constrained diffusion transformer to produce velocity commands, raising video success to 86% and delivering 64.7% real-world navigation on a Unitree G1 humanoid.
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ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning
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Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies
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Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
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Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models
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MemoryWAM: Efficient World Action Modeling with Persistent Memory
MemoryWAM is a world action model with a hybrid memory design using recent frames, anchor frames, and gist tokens for efficient long-horizon robotic manipulation.
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Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing
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A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation
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Introduces SO-101 benchmark for VLA and imitation learning policies on four tasks, showing pretrained VLAs outperform baseline but with high task dependence and execution instability as main failure mode.
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RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning
RDGen uses sim-to-real RL policies to generate smoother robot demonstrations that improve downstream VLA performance over human-collected data on pick-and-place tasks.
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Can Predicted Dynamics Exist in the Physical World?
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JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy
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Vision-Language-Action Models: Experimental Insights from a Real-World UR5 Platform
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RhinoVLA Technical Report
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Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines
A survey of VLA robotics research identifies data infrastructure as the primary bottleneck and distills four open challenges in representation alignment, multimodal supervision, reasoning assessment, and scalable data generation.
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A Tutorial on World Models and Physical AI
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