AGENTIC-SYSTEMSApril 22, 20261 min read10 views

Learning Lifted Action Models from Unsupervised Visual Traces

Researchers have developed a deep learning framework that learns lifted action models directly from sequences of state images without action observations. By integrating a mixed-integer linear program (MILP), the system ensures logical consistency and overcomes the challenges of unsupervised learning in AI planning.

Computer Science > Artificial Intelligence

Title:Learning Lifted Action Models from Unsupervised Visual Traces

View PDF HTML (experimental)Abstract:Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensive prior work has explored learning such models from high-level descriptions of state and/or action sequences. In this paper, we tackle a more challenging setting: learning lifted action models from sequences of state images, without action observation. We propose a deep learning framework that jointly learns state prediction, action prediction, and a lifted action model. We also introduce a mixed-integer linear program (MILP) to prevent prediction collapse and self-reinforcing errors among predictions. The MILP takes the predicted states, actions, and action model over a subset of traces and solves for logically consistent states, actions, and action model that are as close as possible to the original predictions. Pseudo-labels extracted from the MILP solution are then used to guide further training. Experiments across multiple domains show that integrating MILP-based correction helps the model escape local optima and converge toward globally consistent solutions.

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Source: arXiv cs.AI Recent

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