Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks

summary

The gist

The gist Robot task planning in real-world environments requires mapping abstract natural language instructions to executable action sequences under long horizons and complex constraints, and this

In short

The work proposes Any House Any Task (AHAT), a planner for long-horizon robot tasks in large environments with ambiguous human instructions. AHAT uses an LLM to map instructions to symbolic subgoals in PDDL and solves them using explicit reasoning. It introduces Trace-Guided Policy Optimization (TGPO), an RL algorithm that improves subgoal generation by correcting errors in intermediate reasoning traces, leading to significant performance gains over existing methods.

Key concepts

PDDL: Planning Domain Definition Language is a standard language used to describe problems for symbolic planning. In this paper, it is used to define the structured subgoals that the robot needs to achieve. This allows the system to translate abstract human instructions into concrete, solvable symbolic problems that a traditional planner can handle.
Trace-Guided Policy Optimization (TGPO): TGPO is a reinforcement learning method designed for planning tasks where feedback is sparse or noisy. It works by using three mechanisms: outcome-level rewards, correcting errors in the robot's reasoning steps (trace guidance), and applying constraints to the policy updates. This structured approach helps the model generate better, more reliable sequences of subgoals.
Verifier-Grounded Rewards: This reward system provides supervision for the planning policy based on verifiable outcomes. It checks if generated subgoals are actually solvable by a symbolic planner and measures overall task success. This outcome-level feedback guides the model to prioritize generating feasible steps that lead to a successful final plan.
Long-Horizon Planning: Long-horizon planning refers to the ability of an AI system to generate plans that span many sequential steps, often required for complex, abstract human tasks. The paper addresses this by breaking down large goals into a sequence of smaller, manageable subgoals. This decomposition strategy helps manage complexity and prevents error accumulation over extended execution times.

Terminology used across episodes

Reinforcement Learning (RL) — explained in 25 episodes
Federated Learning — explained in 16 episodes
Retrieval-Augmented Generation (RAG) — explained in 16 episodes
Interpretability — explained in 15 episodes
Sample Complexity — explained in 15 episodes
Generalization — explained in 13 episodes

This episode discusses

The paper

Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks · Read on arXiv

Shanghai Innovation Institute · Shanghai Jiao Tong University · Xi’an Jiao Tong University · Nankai University

Open world language conditioned task planning is crucial for robots operating in large-scale household environments. While many recent works attempt to address this problem using Large Language Models (LLMs) via prompting or training, a key challenge remains scalability. Performance often degrades rapidly with increasing environment size, plan length, instruction ambiguity, and constraint complexity. In this work, we propose Any House Any Task (AHAT), a household task planner optimized for long-horizon planning in large environments given ambiguous human instructions. At its core, AHAT utilizes an LLM trained to map task instructions and textual scene graphs into grounded subgoals defined in the Planning Domain Definition Language (PDDL). These subgoals are subsequently solved to generate feasible and optimal long-horizon plans through explicit symbolic reasoning. To enhance the model's ability to decompose complex and ambiguous intentions, we introduce TGPO, a novel reinforcement learning algorithm that integrates external correction of intermediate reasoning traces into Group Relative Policy Optimization (GRPO). Experiments demonstrate that AHAT achieves significant performance gains over state-of-the-art prompting, planning, and learning methods, particularly in human-style household tasks characterized by brief instructions but requiring complex execution plans.

Transcript

Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Any House Any Task".

Dev: The gist Robot task planning in real-world environments requires mapping abstract natural language instructions to executable action sequences under long horizons and complex constraints,

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So we're looking at a paper called "Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks." It's written by Zhihong Liu, Yang Li, Renming Huang, Chendong Zeng, Cewu Lu, and Panpan Cai. It sounds like they’re tackling that tough problem of mapping what we say in plain language to actual robot movements across long times with a lot of tricky rules.

Dev: Yeah it’s about taking those abstract instructions and turning them into something the robot can actually follow, which is hard because standard large language models often struggle with making plans that are both correct and feasible for real-world tasks.

Taro: I wonder if this means we can finally get robots to handle really complex, multi-step instructions without having to manually write out every single step beforehand.

Rosa: Exactly what I was thinking, Taro. The paper suggests a way around the problem of brittle translation into symbolic planning that just gives you a single goal and then struggles with the rest of the task sequence.

Dev: They’re proposing framing robot planning as learning to generate verifiable subgoals in PDDL, which is like giving the AI a structured way to think about the problem before it even tries to execute anything.

The paper's summary: Rosa: So, essentially, the core idea of "Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks" is that instead of relying on a language model to just translate a whole instruction into one big PDDL goal and hoping it works, they learn a policy to generate a sequence of smaller subgoals.

Dev: They are bridging the gap between understanding human language and the precise, executable logic of symbolic planning, which helps make the plan more robust as you get longer or more complicated.

Taro: So if we look at what this does for autonomy, it suggests that instead of getting lost in a massive instruction and failing because one small part doesn't fit, the AI can break it down into pieces that are individually solvable by a planner.

Rosa: That’s right. They treat subgoal generation as something you train with reinforcement learning, which is different from just prompting an LLM to do it once.

Dev: The paper mentions they use a verifier-grounded reward system, which means the robot gets feedback not just on whether the final task succeeded or failed, but specifically on how feasible those intermediate subgoals actually are.

The paper's improvements: Rosa: One of the key things they point out is that decomposing a task into subgoals makes the problem much smaller and less confusing for the planner to handle. It takes something long-horizon and breaks it into modular pieces.

Dev: They are addressing how previous methods, which just tried to translate language directly into a single goal, become brittle when the task gets harder or has more temporal dependencies between steps.

Taro: So, the improvement here is shifting the bottleneck from translating a whole instruction to generating a sequence of subgoals that are individually feasible and chained together correctly.

Rosa: Right. And they introduce Trace-Guided Policy Optimization, or TGPO, which is their reinforcement learning method specifically designed to handle the sparse and noisy feedback you get when training these subgoal generators.

Dev: They have three main parts to this TGPO: first, a reward based on whether the subgoals are solvable; second, a way to correct errors in the intermediate reasoning traces using another model; and third, constrained policy optimization that makes sure the generated steps actually follow sensible rules during training.

Conclusion: Rosa: So looking at the overall picture of "Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks," they’re showing how to make robot task planning scalable by focusing on learning these structured subgoals rather than just generating one big goal.

Dev: The main implication is that we can build systems that handle long, abstract instructions better because the AI learns a systematic way to break things down into steps that a symbolic planner can actually use effectively.

Taro: For someone who just listens to this show, it means future robots won't just be good at simple tasks; they’ll be capable of tackling those really messy, complex instructions we might give them in a real home or workshop setting.

Rosa: Exactly. And before we wrap up this discussion on "Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks," I want to hear from Taro about what you think is the biggest practical hurdle they're still facing with this approach.

Taro: I think the main thing is making sure that when the AI encounters something completely novel, its ability to generalize those low-level skills and combine them into a new sequence of subgoals works reliably.

Dev: And from an engineering side, we have to worry about the loop rate and latency of that whole process; if the trace correction takes too long, it defeats the purpose of real-time planning.

Rosa: That’s a big point. We’ll be looking at how they handle those constraints in our next paper.

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