313 citations · 498 across the 41 of their papers we have counts for
32 papers · 1 filter
Belief-Aware Influence and Trust (BAIT): Shaping Human Belief During Repeated Human-Robot Interaction
Ye-Ji Mun, Mahsa Golchoubian, Shahabedin Sagheb +4
Repeated human-robot interaction (HRI) requires proactively accounting for humans who continually adapt to evolving beliefs about the robot. Prior frameworks often treat encounters…
VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs
Haoran Yuan, Weigang Yi, Zhenyu Zhang +9
Video-Action Models (VAMs) have emerged as a promising framework for embodied intelligence, learning implicit world dynamics from raw video streams to produce temporally consistent…
From Legible to Inscrutable Trajectories: (Il)legible Motion Planning Accounting for Multiple Observers
Ananya Yammanuru, Maria Lusardi, Nancy M. Amato +1
In cooperative environments, such as in factories or assistive scenarios, it is important for a robot to communicate its intentions to observers, who could be either other humans o…
COMMET: A System for Human-Induced Conflicts in Mobile Manipulation of Everyday Tasks
Dongping Li, Shaoting Peng, John Pohovey +1
Continuous advancements in robotics and AI are driving the integration of robots from industry into everyday environments. However, dynamic and unpredictable human activities in da…
Hierarchical Intention Tracking with Switching Trees for Real-Time Adaptation to Dynamic Human Intentions during Collaboration
Zhe Huang, Ye-Ji Mun, Fatemeh Cheraghi Pouria +1
During collaborative tasks, human behavior is guided by multiple levels of intentions that evolve over time, such as task sequence preferences and interaction strategies. To adapt…
A Unified Framework for Robots that Influence Humans over Long-Term Interaction
Shahabedin Sagheb, Sagar Parekh, Ravi Pandya +4
Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human's behavior in a way that help…
EMMOE: A Comprehensive Benchmark for Embodied Mobile Manipulation in Open Environments
Dongping Li, Tielong Cai, Tianci Tang +3
Developing autonomous home robots controlled by natural language has long been a pursuit of humanity. While advancements in large language models (LLMs) and embodied intelligence m…
LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
Zhe Huang, John Pohovey, Ananya Yammanuru +1
Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions to achieve tasks in an open world. However, when…
A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping
Abhi Kamboj, Katherine Driggs-Campbell
Robotic grasping presents a difficult motor task in real-world scenarios, constituting a major hurdle to the deployment of capable robots across various industries. Notably, the sc…
Learning Task Skills and Goals Simultaneously from Physical Interaction
Haonan Chen, Ye-Ji Mun, Zhe Huang +4
In real-world human-robot systems, it is essential for a robot to comprehend human objectives and respond accordingly while performing an extended series of motor actions. Although…
In Situ Soil Property Estimation for Autonomous Earthmoving Using Physics-Infused Neural Networks
W. Jacob Wagner, Ahmet Soylemezoglu, Dustin Nottage +1
A novel, learning-based method for in situ estimation of soil properties using a physics-infused neural network (PINN) is presented. The network is trained to produce estimates of…
DFields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Rearrangement
Yixuan Wang, Mingtong Zhang, Zhuoran Li +5
Scene representation is a crucial design choice in robotic manipulation systems. An ideal representation is expected to be 3D, dynamic, and semantic to meet the demands of diverse…
Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints
Zhe Huang, Hongyu Chen, John Pohovey +1
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing…
Towards Safe Multi-Level Human-Robot Interaction in Industrial Tasks
Zhe Huang, Ye-Ji Mun, Haonan Chen +7
Multiple levels of safety measures are required by multiple interaction modes which collaborative robots need to perform industrial tasks with human co-workers. We develop three in…
User-Friendly Safety Monitoring System for Manufacturing Cobots
Ye-Ji Mun, Zhe Huang, Haonan Chen +4
Collaborative robots are being increasingly utilized in industrial production lines due to their efficiency and accuracy. However, the close proximity between humans and robots can…
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…
Towards Robots that Influence Humans over Long-Term Interaction
Shahabedin Sagheb, Ye-Ji Mun, Neema Ahmadian +4
When humans interact with robots influence is inevitable. Consider an autonomous car driving near a human: the speed and steering of the autonomous car will affect how the human dr…
Seamless Interaction Design with Coexistence and Cooperation Modes for Robust Human-Robot Collaboration
Zhe Huang, Ye-Ji Mun, Xiang Li +6
A robot needs multiple interaction modes to robustly collaborate with a human in complicated industrial tasks. We develop a Coexistence-and-Cooperation (CoCo) human-robot collabora…
Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration
Tan Chen, Zhe Huang, James Motes +13
Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technol…
Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
Tianchen Ji, Arun Narenthiran Sivakumar, Girish Chowdhary +1
Despite the rapid advancement of navigation algorithms, mobile robots often produce anomalous behaviors that can lead to navigation failures. The ability to detect such anomalous b…
Hierarchical Intention Tracking for Robust Human-Robot Collaboration in Industrial Assembly Tasks
Zhe Huang, Ye-Ji Mun, Xiang Li +6
Collaborative robots require effective human intention estimation to safely and smoothly work with humans in less structured tasks such as industrial assembly, where human intentio…
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…
Multi-Agent Variational Occlusion Inference Using People as Sensors
Masha Itkina, Ye-Ji Mun, Katherine Driggs-Campbell +1
Autonomous vehicles must reason about spatial occlusions in urban environments to ensure safety without being overly cautious. Prior work explored occlusion inference from observed…
Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction
Zhe Huang, Ruohua Li, Kazuki Shin +1
Multi-pedestrian trajectory prediction is an indispensable element of autonomous systems that safely interact with crowds in unstructured environments. Many recent efforts in traje…