2 citations · 5 across the 19 of their papers we have counts for
19 papers
WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition
Abigail G. Grassick, Jerome Tze-Hou Hsu, Ethan Lin +10
Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging this data is the high cost o…
From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI
Yongheng Zhang, Ziang Liu, Jiaxuan Zhu +17
Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-im…
Thinking in Video: Can Video Generators Really Reason About the Real World?
Yongheng Zhang, Guang Yang, Ruihan Hou +12
Recent advances in world models and video generation have given rise to an emerging reasoning paradigm that leverages video generative models to simulate, predict, and reason about…
AirDreamer: Generalist Drone Navigation with World Models
Zian Liu, Andong Yang, Chunkai Yang +3
Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot'…
MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
Yuezhang Peng, Chonghao Cai, Ziang Liu +10
Spoken Language Understanding (SLU), which aims to extract user semantics to execute downstream tasks, is a crucial component of task-oriented dialog systems. Existing SLU datasets…
OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving
Xiaoyu Liang, Ziang Liu, Kelvin Lin +10
We present OpenRoboCare, a multimodal dataset for robot caregiving, capturing expert occupational therapist demonstrations of Activities of Daily Living (ADLs). Caregiving tasks in…
EgoCogNav: Cognition-aware Human Egocentric Navigation
Zhiwen Qiu, Ziang Liu, Wenqian Niu +2
Modeling the cognitive and experiential factors of human navigation is central to deepening our understanding of human-environment interaction and to enabling safe social navigatio…
FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization
Rajat Kumar Jenamani, Tom Silver, Ben Dodson +7
Physical caregiving robots hold promise for improving the quality of life of millions worldwide who require assistance with feeding. However, in-home meal assistance remains challe…
Coloring Between the Lines: Personalization in the Null Space of Planning Constraints
Tom Silver, Rajat Kumar Jenamani, Ziang Liu +2
Generalist robots must personalize in-the-wild to meet the diverse needs and preferences of long-term users. How can we enable flexible personalization without sacrificing safety o…
GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings
Ziang Liu, Yuanchen Ju, Yu Da +6
Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical…
REPeat: A Real2Sim2Real Approach for Pre-acquisition of Soft Food Items in Robot-assisted Feeding
Nayoung Ha, Ruolin Ye, Ziang Liu +2
The paper presents REPeat, a Real2Sim2Real framework designed to enhance bite acquisition in robot-assisted feeding for soft foods. It uses `pre-acquisition actions' such as pushin…
An Adaptable, Safe, and Portable Robot-Assisted Feeding System
Ethan Kroll Gordon, Rajat Kumar Jenamani, Amal Nanavati +12
We demonstrate a robot-assisted feeding system that enables people with mobility impairments to feed themselves. Our system design embodies Safety, Portability, and User Control, w…
Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control
Rajat Kumar Jenamani, Daniel Stabile, Ziang Liu +3
Robot-assisted feeding can greatly enhance the lives of those with mobility limitations. Modern feeding systems can pick up and position food in front of a care recipient's mouth f…
Model-Based Control with Sparse Neural Dynamics
Ziang Liu, Genggeng Zhou, Jeff He +4
Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs ar…
Learning to Design and Use Tools for Robotic Manipulation
Ziang Liu, Stephen Tian, Michelle Guo +2
When limited by their own morphologies, humans and some species of animals have the remarkable ability to use objects from the environment toward accomplishing otherwise impossible…
Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks
Eley Ng, Ziang Liu, Monroe Kennedy
Modeling multimodal human behavior has been a key barrier to increasing the level of interaction between human and robot, particularly for collaborative tasks. Our key insight is t…
It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying
Eley Ng, Ziang Liu, Monroe Kennedy
Cooperative table-carrying is a complex task due to the continuous nature of the action and state-spaces, multimodality of strategies, and the need for instantaneous adaptation to…
BEHAVIOR in Habitat 2.0: Simulator-Independent Logical Task Description for Benchmarking Embodied AI Agents
Ziang Liu, Roberto Martín-Martín, Fei Xia +2
Robots excel in performing repetitive and precision-sensitive tasks in controlled environments such as warehouses and factories, but have not been yet extended to embodied AI agent…
Inferring Articulated Rigid Body Dynamics from RGBD Video
Eric Heiden, Ziang Liu, Vibhav Vineet +2
Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains whe…