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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 43Source ↗Scholar ↗
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 248Source ↗Scholar ↗
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 4Source ↗Scholar ↗
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 53Source ↗Scholar ↗
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 42Source ↗Scholar ↗