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Peixin Chang

7 accepted papers

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
2023

A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots

CoRL 2023poster

A command-following robot that serves people in everyday life must continually improve itself in deployment domains with minimal help from its end users, instead of engineers. Previous methods are either difficult to continuously improve after the deployment or require a large number of new labels d…

Cited by 8SourceScholar
2023

Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph

ICRA 2023poster

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. I…

Cited by 92SourceScholar
2023

Learning Visual-Audio Representations for Voice-Controlled Robots

ICRA 2023poster

Based on the recent advancements in representation learning, we propose a novel pipeline for task-oriented voice-controlled robots with raw sensor inputs. Previous methods rely on a large number of labels and task-specific reward functions. Not only can such an approach hardly be improved after the…

Cited by 11SourcecodeScholar
2022

Learning to Navigate Intersections with Unsupervised Driver Trait Inference

ICRA 2022poster

Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits…

Cited by 17SourcecodeScholar
2021

Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning

ICRA 2021poster

Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environme…

Cited by 140SourcecodeScholar
2020

Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control

IROS 2020poster

We explore the interpretation of sound for robot decision making, inspired by human speech comprehension. While previous methods separate sound processing unit and robot controller, we propose an end-to-end deep neural network which directly interprets sound commands for visual-based decision making…

Cited by 11SourceScholar