Papers by Zoya Volovikova

3 papers
AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment (2025.acl-long)

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Challenge: Large Language Models (LLMs) are used for behavior planning given natural language instructions from the user.
Approach: They propose to use a textual dataset of ambiguous instructions addressed to a robot in a kitchen environment to compare them.
Outcome: The proposed dataset includes 1000 pairs of ambiguous tasks and their unambiguous counterparts, with environment descriptions, clarifying questions and answers, user intents, and task plans.
Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning (2026.findings-acl)

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Challenge: a framework for instruction-following tasks is proposed for instruction following tasks . previous methods rely on expert trajectories and learn directly from the agent's own interactions with the environment without expert supervision.
Approach: They propose a framework for instruction-following tasks that enables a language model to generate and refine high-level plans through a self-learning mechanism.
Outcome: The proposed framework adheres to instructions more strictly than baseline methods while showing strong generalization to previously unseen instructions.
CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World (2025.acl-long)

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Challenge: Existing methods to assess instruction following in dynamic and uncertain environments are limited and limited in their ability to adapt to the world's volatility and interdependencies.
Approach: They propose a benchmark for evaluating instruction following in a multimodal environment with diverse instructions and dynamic interactions.
Outcome: The proposed method measures an agent’s ability to generalize to novel instruction formulations and dynamically evolving task configurations, providing a rigorous test of both linguistic understanding and adaptive decision-making.

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