TLDR: ExoPredicator is a new framework for robot planning that learns abstract world models in dynamic environments. It jointly learns symbolic state representations and causal processes for both the robot’s actions (endogenous) and external events (exogenous). By combining variational Bayesian inference with LLM proposals, it learns from limited data and enables fast planning that generalizes to complex, unseen tasks, outperforming existing methods in simulated robotics environments.
Robots face a significant challenge when planning for complex, long-term tasks in dynamic environments. Imagine a robot trying to make coffee or set up dominoes; the world doesn’t just wait for its actions. Things happen on their own – water heats up, dominoes fall – independently of the robot’s direct control. This paper introduces a novel framework called ExoPredicator, designed to help robots reason more effectively in such complex, ever-changing scenarios.
The core idea behind ExoPredicator is to move beyond simulating every tiny detail of the world, like individual pixels or frames. Instead, it focuses on learning “abstract world models.” Think of it like planning a trip: you don’t need to know the exact color of the airplane or the precise millisecond of takeoff to book tickets and plan your journey. Similarly, abstract world models capture only the essential information for decision-making, ignoring irrelevant details.
What makes ExoPredicator unique is its ability to learn two crucial aspects simultaneously: symbolic state representations and causal processes. Symbolic state representations are like a simplified, high-level description of the world, using concepts such as “cup is filled” or “burner is on.” Causal processes, on the other hand, model how things change over time. These processes are divided into two types:
Understanding Causal Processes
Endogenous processes: These are actions directly controlled by the robot, like picking up a jug or switching on a faucet. They represent the robot’s own skills and interventions.
Exogenous processes: These describe events that unfold in the environment on their own, triggered by certain conditions but not requiring continuous intervention from the robot. A classic example is water heating up after a kettle is switched on, or dominoes cascading after the first one is pushed. The robot needs to understand these external dynamics to plan effectively, knowing that it can perform other tasks while waiting for an exogenous process to complete.
ExoPredicator learns these sophisticated world models from limited data, often just a few demonstrations. It uses a combination of advanced techniques, including variational Bayesian inference to handle the uncertainties and complexities of real-world dynamics, and large language models (LLMs) to propose potential symbolic representations and causal structures. The LLMs act as a guide, suggesting plausible ways the world might work, which are then refined and validated through experience.
Once these abstract models are learned, the robot can use them for fast and efficient planning. Instead of simulating every single frame, the system uses a “big-step transition function” that allows it to jump between significant changes in the abstract state. This enables the robot to perform forward searches in the space of high-level actions, considering both its own actions and the concurrent, delayed effects of the environment’s dynamics. For instance, a robot can plan to chop vegetables while waiting for water to boil, knowing that the boiling process will eventually complete on its own.
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Experimental Successes
The effectiveness of ExoPredicator was tested across five simulated tabletop robotics environments, including tasks like filling coffee cups, growing plants, boiling water, setting up dominoes, and navigating a maze with fans. In these experiments, ExoPredicator consistently outperformed a range of existing methods, including hierarchical reinforcement learning, vision-language model planning, and traditional operator learning approaches. It achieved near-perfect success rates in most domains, even when generalizing to new tasks with more objects and more complex goals than seen during training.
The research highlights the critical role of both Bayesian model selection and LLM guidance in learning these complex models efficiently and robustly. Without LLM guidance, the search space for possible causal processes becomes too vast, and without Bayesian model selection, the system lacks a reliable way to choose the best model. The ability to learn new predicates (abstract features) and adapt to varying delays in causal processes also proved essential for its success.
This work represents a significant step towards creating more intelligent and adaptable robots that can reason about and operate in dynamic, real-world environments. By learning to abstract states and causal processes, robots can plan over longer horizons and handle the complexities of concurrent events, moving closer to human-like common-sense reasoning. You can read the full research paper here.
