ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks (1912.01734v2)

Abstract: We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to sequences of actions for household tasks. ALFRED includes long, compositional tasks with non-reversible state changes to shrink the gap between research benchmarks and real-world applications. ALFRED consists of expert demonstrations in interactive visual environments for 25k natural language directives. These directives contain both high-level goals like "Rinse off a mug and place it in the coffee maker." and low-level language instructions like "Walk to the coffee maker on the right." ALFRED tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets. We show that a baseline model based on recent embodied vision-and-language tasks performs poorly on ALFRED, suggesting that there is significant room for developing innovative grounded visual language understanding models with this benchmark.

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

The paper introduces ALFRED, a sophisticated benchmark designed for translating natural language instructions and egocentric vision into actionable sequences for household tasks. Developed to bridge the gap between current research benchmarks and practical, real-world applications, ALFRED presents a comprehensive suite of challenges that align more closely with realistic robotic scenarios.

Dataset Overview

ALFRED consists of 25,743 English language directives which correspond to 8,055 expert demonstrations within the AI2 2.0 simulation environment. These demonstrations are captured in 120 diverse indoor scenes, each providing a unique challenge in terms of object interaction and environment navigation. The tasks are long and complex, involving sequences averaging 50 steps, and cover a wide range of actions including navigation, manipulation, and state changes.

Instructional Complexity

A notable aspect of ALFRED is its incorporation of both high-level goals and step-by-step instructions, providing a nuanced framework for research in grounded language understanding. The language directives are diverse and were collected through crowdsourcing, ensuring a rich variety of phrasing and instruction styles that challenge models to exhibit advanced language comprehension and action mapping capabilities.

Baseline Model Analysis

The authors evaluate a sequence-to-sequence model enhanced with progress monitoring within the challenging environment ALFRED provides. The results indicate a substantial difficulty for existing models, with a maximum goal-condition success rate below 10%, highlighting significant room for advancement. Such performance discrepancies, compared to simpler vision-and-language tasks, emphasize the complexities introduced by long-horizon planning and detailed state management necessary in ALFRED.

Implications and Future Directions

ALFRED sets a high bar for developing models capable of understanding and executing complex task-oriented instructions. The benchmark's inclusion of detailed manipulation and environmental interaction challenges existing models, suggesting potential future directions including more sophisticated hierarchical and modular architectures capable of leveraging the fine-grained language instructions and visual cues provided.

The real-world implications of improvements in this domain are substantial. Effective models on ALFRED could lead to notable advancements in domestic robotics, where robots could perform practical, everyday tasks based on human-like instruction, moving closer to the integration of these systems in homes and workplaces.

Conclusion

ALFRED redefines the expectations of benchmarks at the intersection of vision, language, and robotics. While current models struggle with its demands, the proposed dataset is a step towards the realistic simulation of tasks necessary for the next generation of intelligent, autonomous agents. The research community is provided with a robust framework to innovate solutions that genuinely advance the field of language-grounded robotics.