TEACh: Task-driven Embodied Agents that Chat (2110.00534v3)

Abstract: Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human--human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. The Follower navigates through and interacts with the environment to complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast", asking questions and getting additional information from the Commander. We propose three benchmarks using TEACh to study embodied intelligence challenges, and we evaluate initial models' abilities in dialogue understanding, language grounding, and task execution.

• The paper introduces a dataset of over 3,000 dialogues that guide embodied agents to execute complex tasks in simulated environments.
• The paper develops three benchmarks—EDH, TfD, and TATC—that evaluate dialogue-to-action translation and multi-agent coordination.
• The paper demonstrates that traditional models struggle with dynamic dialogues, underscoring the need for hybrid approaches in embodied AI.

An Analysis of TEACh: Task-driven Embodied Agents that Chat

The TEACh paper introduces a pertinent dataset and benchmarks aimed at advancing the capabilities of embodied AI through natural language dialogue. This paper presents a robust platform for investigating how agents can leverage dialogic interactions to perform complex tasks in simulated environments effectively. The paper revolves around TEACh, a dataset of over 3,000 dialogues where human-human interactions guide task completions within the AI2-THOR simulation environment.

Dataset Overview and Methodology

TEACh stands out as a novel dataset because it emphasizes task-driven, embodied agents capable of understanding and executing tasks through dialogues. The collection process involves two roles: a Commander with oracle knowledge and a Follower who navigates and manipulates the environment. Unlike prior approaches, TEACh dialogues are unconstrained, mimicking the fluidity and spontaneity of human interaction, which poses unique challenges in dialogue understanding and task execution.

The dataset includes diverse tasks ranging from relatively straightforward tasks like "Make Coffee" to more complex hierarchical tasks such as "Prepare Breakfast." Each task is delineated using a task definition language (TDL), allowing for the clear specification of sub-tasks and object states required for successful task completion. This flexibility not only supports current tasks but also facilitates the introduction of new tasks and scenarios, making the dataset extensible.

Benchmark Propositions

Three distinct benchmarks are proposed to evaluate various aspects of embodied dialogue understanding:

1. Execution from Dialogue History (EDH): This requires agents to complete tasks using previously observed dialogues. The challenge here lies in translating past dialogues into present action sequences while accounting for dynamic changes in the environment.
2. Trajectory from Dialogue (TfD): This involves predicting the entirety of the actions performed by the Follower from the dialogue history, akin to predicting a series of navigation and manipulation steps from a stack of instructions.
3. Two-Agent Task Completion (TATC): Involves modeling both the Commander and Follower from start to end. This benchmark explores the integration of dialogue management strategies with action executions.

Experimental Complements and Findings

The initial results from employing the Episodic Transformer (E.T.) model indicate the complexity and challenges inherent in TEACh's benchmarks. Existing models adapting from datasets like ALFRED faced challenges in generalizing their decision-making processes to handle the intricacies of dialogues including out-of-order communications and varying instruction granularity. The passage from isolated, instruction-following agents to those capable of processing dialogue-driven interactions reveals limitations in current AI modeling techniques.

Interestingly, the rule-based agents developed for TATC attain some degree of success in simpler tasks but struggle with more composite tasks. This reinforces the notion that crafty engineering alone is insufficient for mastering the holistic and nuanced interaction within dialogues and environments. As such, TEACh garners a unique platform to test hybrid models that combine learning automated policies with structured reasoning.

Practical and Theoretical Implications

Practically, in the real world, TEACh's benchmarks mirror scenarios where AI intermediaries need to fluidly interpret and perform tasks based on intermittent human inputs. The rich language data, combined with physically interactive actions, positions this research to address real-world problems like autonomous robotics in household and caregiving settings.

Theoretically, TEACh ignites discussions around multi-turn interactions in AI, dialog history handling, anaphora resolution, and grounded semantics. Engaging with such dynamic dialogues demands advancements in neural network architectures, data handling, and dialogue management strategies. As such, future developments may lead to agents with improved dialogue understanding, fostering more natural and effective human-robot collaborations.

Future Directions

Future work could explore compositional learning approaches and few-shot transfer methods to enhance task adaptability and generalization. Extending benchmarks for unseen task configurations and environments could further refine models towards robust agent behaviors. Moreover, facilitating human-in-the-loop actions might bridge the gap between model training and real-time human-machine interactions, translating TEACh's potential into everyday utility.

In conclusion, TEACh presents a substantial leap forward in the ongoing discourse of deploying dialogue-driven embodied agents. Continuing to leverage and extend this dataset will invigorate a broad range of applications where AI must operate seamlessly and intelligently through the medium of conversation.