TL;DR
This work introduces tentative object pruning, an approach that leverages this property by constructing multiple simplified tasks with reduced object sets and searching them in parallel until a valid, satisficing solution is found.
GATS (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance, is presented.
This work reframe trajectory evaluation as a distance between the agent's execution graph and a set of valid solution graphs, and instantiates OTAP via an unbalanced fused Gromov-Wasserstein transport problem over attributed dependency graphs, which is a pseudo-metric that is provably invariant to dependency-preserving reorderings and has bounded sensitivity to redundant steps.
Evidence Acquisition and Feasibility Gating (EAFG) is proposed, a framework that acquires visual evidence through VLM-generated exploratory subgoals and TAMP-based execution and applies a feasibility gate to decide whether to proceed with task planning, acquire further evidence, or halt.
This paper focuses on the problem of Embodied Task Planning, where an agent is required to execute a sequence of atomic actions within an interactive environment to complete a user-specified task. Though a variety of simulators and datasets have previously been built for this task, these efforts are largely isolated, with each using its own observation format, action type, and task domain. This fragmentation complicates comprehensive model evaluation and hinders the scalability of training data. As an effort towards generalizable embodied planning, we propose UniETP, a unified interface integrating four commonly-used simulators (AI2-THOR, VirtualHome, Habitat, BEHAVIOR). UniETP is characterized by both standardization and diversity. On one hand, it formalizes all the simulators into a consistent observation and action space, and builds an evaluation system to support complicated task goal. On the other hand, it enhances task diversity and complexity across dimensions like task logic, instance grounding, and instruction understanding, constructing a new dataset with varied levels of difficulty in an automatic manner. Extensive experiments on the proposed benchmark are conducted to evaluate the embodied planning capabilities of recent models and analyze the performance bottlenecks. Codes and data will be available at https://github.com/woyut/UniETP .