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Tsang-Wei Edward Lee

7 accepted papers

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…

2024

Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs

CoRL 2024poster

An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation wi…

Cited by 20SourceScholar
2024

PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

ICML 2024poster

Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce only textual outputs, while robotic control and oth…

Cited by 95SourcePDFScholar
2023

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

CoRL 2023poster

We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions a…

Cited by 1068SourceScholar
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
2020

Learned Critical Probabilistic Roadmaps for Robotic Motion Planning

ICRA 2020poster

Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samples to construct an implicit graph representation of the robot's state space, allowing arbitrarily accurate representati…

Cited by 107SourceScholar
2019

Long Range Neural Navigation Policies for the Real World

IROS 2019poster

Learned Neural Network based policies have shown promising results for robot navigation. However, most of these approaches fall short of being used on a real robot due to the extensive simulated training they require. These simulations lack the visuals and dynamics of the real world, which makes it…

Cited by 22SourceScholar