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Justin Lin

7 accepted papers

2026

How to guide your flow: Steering flow maps for rapid test-time alignment

ICML 2026poster

In generative modeling, we often wish to produce samples that satisfy a user-specified reward such as measurement consistency, aesthetic quality, or alignment with human intent, a problem known as inference-time guidance. While flow-based models enable high-quality generation, existing guidance meth…

Cited by 0SourceScholar
2025

VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks

ICLR 2025poster

Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text or static imagery can provide. However, many existing agent benchmarks neglect long-context video understanding, instead focus- ing on text or static image inputs. To bridge this ga…

Cited by 3SourcePDFScholar
2024

DRAGON: A Dialogue-Based Robot for Assistive Navigation With Visual Language Grounding

RA-L 2024

Persons with visual impairments (PwVI) have difficulties understanding and navigating spaces around them. Current wayfinding technologies either focus solely on navigation or provide limited communication about the environment. Motivated by recent advances in visual-language grounding and semantic n

Cited by 32SourcecodeScholar
2019

Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching

ICRA 2019poster

Much of the literature on robotic perception focuses on the visual modality. Vision provides a global observation of a scene, making it broadly useful. However, in the domain of robotic manipulation, vision alone can sometimes prove inadequate: in the presence of occlusions or poor lighting, visual…

Cited by 74SourceScholar
2018

More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch

RA-L 2018

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this letter, we investigate how a robot can learn to use t

Cited by 396SourceScholar
2017

The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?

CoRL 2017

A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more succes

Cited by 0SourcePDFScholar