Design choices shape world action models for robot control
What Matters in Designing World Action Models: An Empirical Study
Robotics
Summary
Controlling robots effectively requires models that understand the world and decide actions. This paper studies key design choices in these models, like how the robot thinks about cause and effect, where it stores information, and how it's trained. The authors ran many controlled tests on robot simulation tasks and real robot data to see which designs work best and why. Their findings help guide future robot control system designs by clarifying what factors really matter.
What this means in practice
- •For robotic system developers: Choose optimal model designs for improved generalizable robot controls across different tasks and environments.
- •For industrial automation teams: Implement better action models for robots operating in dynamic factory settings to enhance adaptability and performance.
Authors
Chao Tang, Haoqing Wang, Zilang Cen, Weishi Mi, Wei Xia, Fangcheng Liu, Anda Cheng, Yeqing Shen, Xiaohui Cui, Xiaoyuan Zhang, Yehui Tang, Tingguang Li
Abstract
World Action Models (WAMs) have emerged as a promising paradigm for generalizable robot control. Despite the growing number of WAM systems, existing works often introduce unified systems that bundle together multiple design choices, such as architecture and training strategy, making it difficult to isolate individual contributions and systematically compare alternative designs. In this work, we present a controlled study that disentangles these design choices and analyzes not only their empirical effects, but also how and why they shape WAMs. More specifically, we focus on three fundamental questions in building WAMs: (1) what causal structure should govern the interaction between world modeling and action generation? (2) in which latent space should world modeling be performed? and (3) how do different world-action modeling objectives affect model behavior and performance? Through structurally controlled experiments on three representative benchmarks, RoboCasa-GR1, LIBERO, and LIBERO-Plus, we systematically compare six causal structures, eight latent representations, and four training objectives, covering popular design choices in existing WAMs. We further validate our key findings on real-robot data from the DROID dataset. We hope to provide a systematic understanding of how core design choices affect world-action modeling and what principles can guide the development of future WAM systems.