UniETP: Unifying Environments for Generalizable Embodied Task Planning
UniETP unifies four simulators with standardized observation/action spaces, enabling diverse task generation for generalizable embodied planning.
Key Findings
Methodology
UniETP integrates AI2-THOR, VirtualHome, Habitat, and BEHAVIOR via a unified Python interface, defining consistent observation (local view, global info, feedback) and action spaces (navigation, manipulation). It employs a scene graph (USG) for environment abstraction and supports complex task evaluation through a logic system. An automatic task generation pipeline leverages large language models (LLMs) and a commonsense knowledge base to produce over 500 tasks with varying difficulty levels, covering atomic, compositional, logical, linguistic, and instance-grounded tasks. Experiments evaluate recent vision-language models' performance across these tasks, demonstrating improved transferability and robustness.
Key Results
- The benchmark results show that state-of-the-art models improve success rates by approximately 15%, reaching around 75% on complex tasks. The automatic task generation successfully created over 500 diverse instances, enabling detailed evaluation of model understanding and reasoning. Cross-environment testing confirms strong generalization capabilities, although performance gaps remain in fine-grained perception and complex logical reasoning, indicating areas for future improvement.
- Models exhibit notable transferability across simulators, validating UniETP's environment unification. The results highlight the importance of environment standardization and task diversity in training robust embodied agents. The experiments also reveal that models struggle with instance-level grounding and complex instruction parsing, emphasizing the need for enhanced perception and language understanding modules.
- Overall, UniETP provides a scalable, flexible platform that significantly advances the evaluation of embodied planning models, fostering development of more capable, generalizable robotic systems.
Significance
UniETP addresses the fragmentation caused by isolated simulation environments, creating a unified platform that promotes cross-environment training and evaluation. Its standardized interface reduces development barriers, accelerates research, and enables models to better transfer knowledge across diverse scenarios. The rich, automatically generated task set supports comprehensive assessment of models’ reasoning, perception, and planning abilities, pushing the frontier of embodied AI. This work lays a foundation for future integration of large models with embodied systems, facilitating progress toward autonomous robots capable of operating seamlessly in real-world environments, such as homes, warehouses, and public spaces.
Technical Contribution
The study introduces a unified simulation interface, standardizing observation and action spaces across four major simulators, and employs scene graph abstraction for environment modeling. The automatic task generation pipeline combines large language models and a commonsense knowledge base to produce a wide variety of tasks, from simple to complex. The logical system for task evaluation enables precise, flexible goal specification, accommodating complex logical and temporal constraints. These innovations collectively reduce environment-specific dependencies, enhance scalability, and support multi-task, multi-scene training, representing a significant step forward in embodied AI research.
Novelty
UniETP is the first framework to unify four major simulators with a standardized interface, enabling cross-platform training and evaluation. Its automatic task generation mechanism, leveraging LLMs and knowledge bases, creates a highly diverse and scalable dataset that covers multiple task complexities. Unlike prior benchmarks limited to single environments or simple tasks, UniETP supports complex logical, linguistic, and instance-grounded tasks, pushing the boundaries of current embodied planning research. This comprehensive integration and automation mark a new milestone in the field.
Limitations
- Despite环境标准化，模型在真实场景中的鲁棒性和感知能力仍有限，尤其在复杂、多变的实际环境中表现不足。未来需结合真实数据和多模态感知技术，提升系统适应性。
- 自动任务生成依赖大模型，可能引入语义偏差或生成不稳定的问题，影响任务真实性和多样性。需要改进生成机制以确保质量和多样性。
- 目前实验主要在模拟环境中进行，实际应用中面对传感噪声、环境变化等挑战，系统的泛化能力和鲁棒性仍需验证。
Future Work
未来将结合真实机器人平台，验证系统在实际环境中的表现，增强多模态感知能力。计划引入强化学习和自主探索策略，提升模型的自主学习和适应能力。还将优化任务生成流程，丰富任务类型，推动从模拟到现实的迁移研究。
AI Executive Summary
In recent years, embodied AI has gained significant momentum, aiming to develop autonomous agents capable of understanding and executing complex tasks within interactive environments. Existing simulation platforms like AI2-THOR, VirtualHome, Habitat, and BEHAVIOR have provided valuable testbeds, but their isolated nature hampers the development of truly generalizable models. Researchers face challenges in transferring learned skills across environments due to inconsistent observation and action interfaces, limiting scalability and robustness.
To address these issues, UniETP introduces a unified framework that integrates four major simulators through a standardized Python interface. This unification defines consistent observation spaces—including egocentric views, global scene information, and feedback signals—and a comprehensive action space covering navigation and manipulation. The core innovation lies in employing a scene graph (USG) abstraction, which models environment objects and their spatial relations in a simulator-agnostic manner, enabling consistent environment understanding and evaluation.
Building on this foundation, UniETP leverages large language models (LLMs) and a commonsense knowledge base to automatically generate a diverse set of over 500 tasks, spanning simple atomic actions to complex, long-horizon, logical, and instance-grounded tasks. This automatic pipeline ensures rich, scalable datasets that facilitate multi-level evaluation of models’ reasoning, perception, and planning capabilities. Experiments demonstrate that recent vision-language models achieve success rates around 75% on these tasks, with notable improvements over prior benchmarks, and exhibit promising transferability across simulators.
The significance of UniETP extends beyond technical innovation; it provides a scalable, flexible platform to accelerate embodied AI research. By standardizing environments and enriching task diversity, it reduces barriers to model development and evaluation, fostering progress toward autonomous agents capable of operating seamlessly in real-world scenarios. Despite these advances, challenges remain in enhancing perception robustness and real-world applicability. Future work will focus on integrating real robot experiments, improving multi-modal sensing, and advancing autonomous exploration strategies, paving the way for truly intelligent, adaptable robots in everyday life.
Deep Dive
Abstract
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 .