Do World Action Models Generalize Better than VLAs? A Robustness Study
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response to actions. Vision-language-action (VLA), which repurpose large-scale vision-language models for robot action generation using action experts, have achieved notable success across a variety of robotic tasks. Nevertheless, their performance remains constrained by the scope of their training data, exhibiting limited generalization to unseen scenarios and vulnerability to diverse contextual perturbations. More recently, world models have been revisited as an alternative to VLAs. These models, referred to as world action models (WAMs), are built upon world models that are trained on large corpora of video data to predict future states. With minor adaptations, their latent representation can be decoded into robot actions. It has been suggested that their explicit dynamic prediction capacity, combined with spatiotemporal priors acquired from web-scale video pretraining, enables WAMs to generalize more effectively than VLAs. In this paper, we conduct a comparative study of prominent state-of-the-art VLA policies and recently released WAMs. We evaluate their performance on the LIBERO-Plus and RoboTwin 2.0-Plus benchmarks under various visual and language perturbations. Our results show that WAMs achieve strong robustness, with LingBot-VA reaching 74.2% success rate on RoboTwin 2.0-Plus and Cosmos-Policy achieving 82.2% on LIBERO-Plus. While VLAs such as π_{0.5} can achieve comparable robustness on certain tasks, they typically require extensive training with diverse robotic datasets and varied learning objectives. Hybrid approaches that partially incorporate video-based dynamic learning exhibit intermediate robustness, highlighting the importance of how video priors are integrated.
69d3aa592c523d36431fae43
Zhanguang Zhang
69d3aa592c523d36431fae44
Zhiyuan Li
69d3aa592c523d36431fae45
Behnam Rahmati
69d3aa592c523d36431fae46
Rui Heng Yang
69d3aa592c523d36431fae47
Yintao Ma
69d3aa592c523d36431fae48
Amir Rasouli
69d3aa592c523d36431fae49
Sajjad Pakdamansavoji
69d3aa592c523d36431fae4a
Yangzheng Wu
69d3aa592c523d36431fae4b
Lingfeng Zhang
69d3aa592c523d36431fae4c
Tongtong Cao
69d3aa592c523d36431fae4d
Feng Wen
69d3aa592c523d36431fae4e
Xinyu Wang
69d3aa592c523d36431fae4f
Xingyue Quan
69d3aa592c523d36431fae50
Yingxue Zhang
Do World Action Models Generalize Better than VLAs? A Robustness Study
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response to actions. Vision-language-action (VLA), which repurpose large-scale vision-language models for robot action generation using action experts, have achieved notable success across a variety of robotic tasks. Nevertheless, their performance remains constrained by the scope of their training data, exhibiting limited generalization to unseen scenarios and vulnerability to diverse contextual perturbations. More recently, world models have been revisited as an alternative to VLAs. These models, referred to as world action models (WAMs), are built upon world models that are trained on large corpora of video data to predict future states. With minor adaptations, their latent representation can be decoded into robot actions. It has been suggested that their explicit dynamic prediction capacity, combined with spatiotemporal priors acquired from web-scale video pretraining, enables WAMs to generalize more effectively than VLAs. In this paper, we conduct a comparative study of prominent state-of-the-art VLA policies and recently released WAMs. We evaluate their performance on the LIBERO-Plus and RoboTwin 2.0-Plus benchmarks under various visual and language perturbations. Our results show that WAMs achieve strong robustness, with LingBot-VA reaching 74.2% success rate on RoboTwin 2.0-Plus and Cosmos-Policy achieving 82.2% on LIBERO-Plus. While VLAs such as π_{0.5} can achieve comparable robustness on certain tasks, they typically require extensive training with diverse robotic datasets and varied learning objectives. Hybrid approaches that partially incorporate video-based dynamic learning exhibit intermediate robustness, highlighting the importance of how video priors are integrated.
World action models demonstrate superior robustness in robot action planning compared to vision-language-action models, achieving higher success rates on benchmark datasets under various perturbations.
vision-language-action world models world action models latent representation spatiotemporal priors video pretraining dynamic prediction capacity LIBERO-Plus RoboTwin 2.0-Plus