121 citations · 167 across the 22 of their papers we have counts for
22 papers
ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusion
Zichao Hu, Chen Tang, Michael J. Munje +6
This paper considers the problem of enabling robots to navigate dynamic environments while following instructions. The challenge lies in the combinatorial nature of instruction spe…
MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos
Rutav Shah, Shuijing Liu, Qi Wang +5
We aim to enable humanoid robots to efficiently solve new manipulation tasks from a few video examples. In-context learning (ICL) is a promising framework for achieving this goal d…
SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation
Michael J. Munje, Chen Tang, Shuijing Liu +6
Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-Language Models (VLMs) exhibit…
Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu +6
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A…
Tool-as-Interface: Learning Robot Policies from Observing Human Tool Use
Haonan Chen, Cheng Zhu, Shuijing Liu +2
Tool use is essential for enabling robots to perform complex real-world tasks, but learning such skills requires extensive datasets. While teleoperation is widely used, it is slow,…
Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models
Haonan Chen, Jiaming Xu, Lily Sheng +4
When performing tasks like laundry, humans naturally coordinate both hands to manipulate objects and anticipate how their actions will change the state of the clothes. However, ach…
HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments
Shuijing Liu, Haochen Xia, Fatemeh Cheraghi Pouria +5
We study the problem of robot navigation in dense and interactive crowds with static constraints such as corridors and furniture. Previous methods fail to consider all types of spa…
Topology-Guided ORCA: Smooth Multi-Agent Motion Planning in Constrained Environments
Fatemeh Cheraghi Pouria, Zhe Huang, Ananya Yammanuru +2
We present Topology-Guided ORCA as an alternative simulator to replace ORCA for planning smooth multi-agent motions in environments with static obstacles. Despite the impressive pe…
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation
Shuijing Liu, Kaiwen Hong, Neeloy Chakraborty +1
We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of t…
Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks
Haonan Chen, Yilong Niu, Kaiwen Hong +4
Stowing, the task of placing objects in cluttered shelves or bins, is a common task in warehouse and manufacturing operations. However, this task is still predominantly carried out…
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…
Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection
Neeloy Chakraborty, Aamir Hasan, Shuijing Liu +4
In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that mo…
Designing a Wayfinding Robot for People with Visual Impairments
Shuijing Liu, Aamir Hasan, Kaiwen Hong +6
People with visual impairments (PwVI) often have difficulties navigating through unfamiliar indoor environments. However, current wayfinding tools are fairly limited. In this short…
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…
Occlusion-Aware Crowd Navigation Using People as Sensors
Ye-Ji Mun, Masha Itkina, Shuijing Liu +1
Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the highly dynamic, partially observable environment. Occlusions are highly prevalent in such set…
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…
Off Environment Evaluation Using Convex Risk Minimization
Pulkit Katdare, Shuijing Liu, Katherine Driggs-Campbell
Applying reinforcement learning (RL) methods on robots typically involves training a policy in simulation and deploying it on a robot in the real world. Because of the model mismat…
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…
Robust Deep Reinforcement Learning with Adversarial Attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu +2
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties…