22 citations · 28 across the 7 of their papers we have counts for
7 papers
DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding
Shuijing Liu, Aamir Hasan, Kaiwen Hong +7
Persons with visual impairments (PwVI) have difficulties understanding and navigating spaces around them. Current wayfinding technologies either focus solely on navigation or provi…
A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots
Peixin Chang, Shuijing Liu, Tianchen Ji +3
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. Prev…
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
Shuijing Liu, Peixin Chang, Zhe Huang +6
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 differen…
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Shuijing Liu, Peixin Chang, Haonan Chen +2
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 s…
Learning Visual-Audio Representations for Voice-Controlled Robots
Peixin Chang, Shuijing Liu, D. Livingston McPherson +1
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…
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Shuijing Liu, Peixin Chang, Weihang Liang +2
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…
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control
Peixin Chang, Shuijing Liu, Haonan Chen +1
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…