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Leila Takayama

7 accepted papers

2025

Achieving Human Level Competitive Robot Table Tennis

ICRA 2025

Achieving human-level performance on real world tasks is a north star for the robotics community. We present the first learned robot agent that reaches amateur humanlevel performance in competitive table tennis. Table tennis is a physically demanding sport that takes humans years to master. We contr

Cited by 43SourceScholar
2024

Learning to Learn Faster from Human Feedback with Language Model Predictive Control

RSS 2024poster

Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new tasks. However, these capabilities (driven by in-co…

2023

Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners

CoRL 2023oral

Large language models (LLMs) exhibit a wide range of promising capabilities --- from step-by-step planning to commonsense reasoning --- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, a framework for measuring and aligni…

Cited by 248SourceScholar
2022

A-RIFT: Visual Substitution of Force Feedback for a Zero-Cost Interface in Telemanipulation

IROS 2022poster

We present an accessible robot interface for telemanipulation (A-RIFT), which preserves the haptic channel partially in a zero-additional-cost interface by visual substitution of force feedback (VSFF). This work explores a gap in the literature, resulting from the focus on performance improvements i…

Cited by 4SourceScholar
2022

Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation

CoRL 2022poster

Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied public spaces. To address this, we present a new class of implicit control policies combining the benefits of imitation lear…

Cited by 53SourceScholar
2021

Modeling Human Helpfulness with Individual and Contextual Factors for Robot Planning

RSS 2021poster

Robots deployed in human-populated spaces often need human help to effectively complete their tasks. Yet; a robot that asks for help too frequently or at the wrong times may cause annoyance; and a robot that asks too infrequently may be unable to complete its tasks. In this paper; we present a model…

2020

Are We There Yet? Comparing Remote Learning Technologies in the University Classroom

RA-L 2020

Telepresence robots can empower people to work, play, and learn along with others, despite geographic distance. To investigate the use of telepresence robots for remote attendance of university-level classes, we conducted a study in four courses at our university. We compared student experiences att

Cited by 42SourceScholar