ICAPS 2026 Summer School

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For over six decades, automated planning has been central to AI, enabling systems to reason about actions and achieve goals in complex, dynamic environments. As large language models (LLMs) redefine what AI systems can do, planning ideas are becoming newly relevant: they offer principled ways to specify objectives and constraints, evaluate behavior, and move from fluent language to reliable, goal-directed decision making.

This lecture focuses on methods for planning with language models. The first part covers plan generation with LLMs, treating the model as a planner. We will cover both post-training and inference. The second part explores hybrid approaches that combine LLMs with symbolic planning components at build time, inference time, or both, to improve correctness, scalability, and robustness.

For over six decades, automated planning has been central to AI, enabling systems to reason about actions and achieve goals in complex, dynamic environments. As large language models (LLMs) redefine what AI systems can do, planning ideas are becoming newly relevant: they offer principled ways to specify objectives and constraints, evaluate behavior, and move from fluent language to reliable, goal-directed decision making.

This lecture focuses on methods for planning with language models. The first part covers plan generation with LLMs, treating the model as a planner. We will cover both post-training and inference. The second part explores hybrid approaches that combine LLMs with symbolic planning components at build time, inference time, or both, to improve correctness, scalability, and robustness.

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Many decision problems come with rich, structured knowledge of dynamics and rewards -- so why discard that structure and go model-free? This lab makes the case for model-based probabilistic planning and walks through the full pipeline: formal modeling, an interactive environment, and a differentiable solver. We’ll start with a concise, pragmatic refresher on MDPs and stochastic planning, positioning RDDL relative to PPDDL and highlighting support for concurrency, numeric/Boolean fluents, conditional effects, and exogenous processes. We then introduce pyRDDLGym, which compiles RDDL descriptions into OpenAI Gym–compatible environments for fast simulation, visualization, and benchmarking.

The centerpiece is a hands-on Colab session where participants will:
(i) model a stochastic control task in RDDL;
(ii) generate and explore a pyRDDLGym environment; and
(iii) experiment with the default random agent and a planning-by-backpropagation solver—optimizing a parameterized policy by differentiating through model rollouts, bridging planning and learning.

By the end, participants will be able to model and solve stochastic planning problems using RDDL and pyRDDLGym, and gain understanding when explicit models can outperform black-box methods.

Scheduling is about deciding when and with what resources set of tasks should be performed. Unlike planning, we typically know what tasks are to be performed, or perhaps must select them from some options, or the tasks are given to us over time in an online setting. However, we do not typically have to decide what actions to perform. Scheduling is widely studied in Operations Research (OR), theoretical computer science, computer systems, and queueing theory, as well as in AI, leading to a very large variety of problems and solution approaches. In this tutorial, I will provide a brief overview of this scope and then focus on exact techniques for problems studied in OR and AI: mixed-integer linear programming, constraint programming, and recent work on using heuristic search to solve scheduling problems within the domain-independent dynamic programming framework.

You’re a new robot, just coming online. How do you move your body? What’s surrounding you? What are you supposed to do? Come to this session to find out!

In reinforcement learning (RL) a physical or virtual agent needs to learn about its surroundings while also figuring out how to maximize a reward. These sessions will help you understand
1) how to identify when RL could be the right framing for a problem,
2) what makes an RL problem more or less difficult to solve,
3) how a handful of simple RL algorithms work, and
4) where to go if you’d like to learn more.

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Automated planning is especially challenging when state and action spaces are continuous, time horizons are long, environments are constrained, and feedback is sparse---all of which are common in robotics. Task and motion planning (TAMP) addresses these challenges by enabling the agent to reason jointly about “what to do” (task planning) and “how to do it” (motion planning). These two levels of reasoning are often entangled due to the lossy nature of the task-planning abstractions that translate the continuous robot environments into discrete representations. In this tutorial and lab, we will approach these challenges with a first-principles introduction to TAMP. Participants should come away with new intuitions and practical tools---an understanding of both “what to do” and “how to do it” when decision-making calls for TAMP.

Automated planning is especially challenging when state and action spaces are continuous, time horizons are long, environments are constrained, and feedback is sparse---all of which are common in robotics. Task and motion planning (TAMP) addresses these challenges by enabling the agent to reason jointly about “what to do” (task planning) and “how to do it” (motion planning). These two levels of reasoning are often entangled due to the lossy nature of the task-planning abstractions that translate the continuous robot environments into discrete representations. In this tutorial and lab, we will approach these challenges with a first-principles introduction to TAMP. Participants should come away with new intuitions and practical tools---an understanding of both “what to do” and “how to do it” when decision-making calls for TAMP.

TBD