Whale-X: Learning Scalable Embodied World Models with Enhanced Generalizability
world model;sequential decision-making;embodied control
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@misc{
zhang2024whalex,
title={Whale-X: Learning Scalable Embodied World Models with Enhanced Generalizability},
author={Zhilong Zhang and Ruifeng Chen and Junyin Ye and Yihao Sun and Pengyuan Wang and Jing-Cheng Pang and Kaiyuan Li and Tian-Shuo Liu and Haoxin Lin and Yang Yu},
year={2024},
url={https://openreview.net/forum?id=SH3DTbytqF}
}
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. However, to support faithful imagination in out-of-distribution (OOD) regions, world models must possess significant generalizability, which poses substantial challenges for previous scalable approaches. This paper addresses two primary sources of the world model generalization error: the \emph{policy distribution shift} caused by the divergence between test and data-collection policies, and the \emph{compounding error} arising from long-horizon autoregressive rollout. To tackle these issues, we introduce the \emph{policy-conditioning} and the \emph{retracing-rollout} techniques, respectively. Incorporating these two techniques, we present Whale, a scalable spatial-temporal transformer-based world model with enhanced generalizability. We first demonstrate the effectiveness of the two techniques, showcasing their consistent superiority over previous baselines in both trajectory generation quality and value estimation accuracy. Furthermore, we propose Whale-X, a 414M parameter world model trained on 970K trajectories from Open X-Embodiment datasets. We show that Whale-X exhibits promising scalability and strong generalizability in real-world manipulation scenarios using minimal demonstrations.
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~Zhilong_Zhang2 ~Ruifeng_Chen1 ~Junyin_Ye1 ~Yihao_Sun1 ~Pengyuan_Wang3 ~Jing-Cheng_Pang1 ~Kaiyuan_Li2 ~Tian-Shuo_Liu1 ~Haoxin_Lin1 ~Yang_Yu5
Zhilong Zhang Ruifeng Chen Junyin Ye Yihao Sun Pengyuan Wang Jing-Cheng Pang Kaiyuan Li Tian-Shuo Liu Haoxin Lin Yang Yu
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world model sequential decision-making embodied control
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zhang|whalex_learning_scalable_embodied_world_models_with_enhanced_generalizability
/pdf/dd496630c037cb466e3155003711d9db35cbbe15.pdf
reinforcement learning
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Whale-X: Learning Scalable Embodied World Models with Enhanced Generalizability
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