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Ifrah Idrees

4 accepted papers

2025

λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics

Ahmed Jaafar, Shreyas Sundara Raman, Sudarshan S. Harithas, Yichen Wei, Sofia Juliani, Anneke Wernerfelt +4
IROS 2025

Learning to execute long-horizon mobile manipulation tasks is crucial for advancing robotics in household and workplace settings. However, current approaches are typically data-inefficient, underscoring the need for improved models that require realistically sized benchmarks to evaluate their effici

Cited by 4Source ↗Scholar ↗
2024

CAPE: Corrective Actions from Precondition Errors using Large Language Models

Shreyas Sundara Raman, Vanya Cohen, Ifrah Idrees, Eric Rosen, Raymond Mooney, Stefanie Tellex +1
ICRA 2024poster

Extracting knowledge and reasoning from large language models (LLMs) offers a path to designing intelligent robots. Common approaches that leverage LLMs for planning are unable to recover when actions fail and resort to retrying failed actions without resolving the underlying cause. We propose a nov…

Cited by 34Source ↗Scholar ↗
2023

Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments

Jason Xinyu Liu, Ziyi Yang, Ifrah Idrees, Sam Liang, Benjamin Schornstein, Stefanie Tellex +1
CoRL 2023poster

Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in…

Cited by 46Source ↗Scholar ↗
2023

Improved Inference of Human Intent by Combining Plan Recognition and Language Feedback

Ifrah Idrees, Tian Yun, Naveen Sharma, Yunxin Deng, Nakul Gopalan, George Konidaris +1
IROS 2023poster

Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with people effectively, robots must recognize human plans and goals from noisy observatio…

Cited by 1Source ↗Scholar ↗