Stable Planning through Aligned Representations (SPAR), an algorithm that explicitly captures invariances by training an alignment network to map noisy states to the latent representation of clean states while keeping the learned world model fixed, is introduced.
This work presents a novel world model formulation where the reward prediction only depends on a subset of structured, symbolic components of the whole latent state, and demonstrates the strong generalisation properties of this approach over purely neural methods.
Isidoro Tamassia, Lennert De Smet, Giuseppe Marra· 0 citations
To the knowledge, RP1 is the first method to fully learn how to improve multi-step plans and can be trained independently of and attached to any pretrained latent world model.
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design of this space through controlled comparisons, finding that neither reconstruction fidelity nor pre-trained perceptual features alone ensure e...
Hao-Yi Jiang, Liu Liu, Xin-Jiang Wang et al.· 1 citation
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are w...
Min-Gu Kang, Yoori Oh, Sookyung Kim et al.· 0 citations
The proposed Representation World Model, which learns states, transitions, and executable plans directly in representation space, suggests that planning directly in representation space provides a promising alternative to conventional world-model planning.
Yi-Jun Yuan, Wei-Cheng Zheng, Wei-Bang Wang et al.· 0 citations
Driving world models learn rich predictive representations of the surrounding environment from visual observations, yet accurate visual prediction does not necessarily translate into effective trajectory planning. We argue that a key bottleneck lies in the mismatch between visual world states and raw geometric trajecto...
Ming-Kai Jia, Jia-Xin Guo, Zhi-Jian Shu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
