Hangxin Liu

Hangxin Liu
University of California, Los Angeles | UCLA · Department of Computer Science

PhD Student at University of California, Los Angeles

About

39
Publications
8,742
Reads
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466
Citations
Citations since 2017
36 Research Items
467 Citations
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2017201820192020202120222023050100150
2017201820192020202120222023050100150

Publications

Publications (39)
Article
Full-text available
We present a novel Augmented Reality (AR) interface to provide effective means to diagnose a robot's erroneous behaviors, endow it with new skills, and patch its knowledge structure represented by an And-Or Graph (AOG). Specifically, an AOG representation of opening medicine bottles is learned from human demonstration and yields a hierarchical stru...
Article
We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training. Leveraging a Finite Element Method (FEM)-based simulator that reproduces fine-grained, continuous visual and physical effects given observed tool-use events, the essential...
Article
Full-text available
In this paper, we rethink the problem of scene reconstruction from an embodied agent’s perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints of the reconstructed scenes that provide actionable information for simulating interactions with agents. Here, we a...
Conference Paper
Full-text available
Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing unde- sired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexi...
Preprint
Full-text available
Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity...
Preprint
Full-text available
We devise a 3D scene graph representation, contact graph+ (cg+), for efficient sequential task planning. Augmented with predicate-like attributes, this contact graph-based representation abstracts scene layouts with succinct geometric information and valid robot-scene interactions. Goal configurations, naturally specified on contact graphs, can be...
Preprint
We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training. Leveraging a Finite Element Method (FEM)-based simulator that reproduces fine-grained, continuous visual and physical effects given observed tool-use events, the essential...
Article
Full-text available
We devise a cooperative planning framework to generate optimal trajectories for a robot duo tethered by a flexible net to gather scattered objects spread in a large area. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterati...
Preprint
Full-text available
We devise a cooperative planning framework to generate optimal trajectories for a tethered robot duo, who is tasked to gather scattered objects spread in a large area using a flexible net. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we fo...
Preprint
Full-text available
We construct a Virtual Kinematic Chain (VKC) that readily consolidates the kinematics of the mobile base, the arm, and the object to be manipulated in mobile manipulations. Accordingly, a mobile manipulation task is represented by altering the state of the constructed VKC, which can be converted to a motion planning problem, formulated, and solved...
Preprint
Full-text available
We present a Virtual Kinematic Chain (VKC) perspective, a simple yet effective method, to improve task planning efficacy for mobile manipulation. By consolidating the kinematics of the mobile base, the arm, and the object being manipulated collectively as a whole, this novel VKC perspective naturally defines abstract actions and eliminates unnecess...
Preprint
Full-text available
In this paper, we rethink the problem of scene reconstruction from an embodied agent's perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints such that the reconstructed scenes provide \em{actionable} information for simulating \em{interactions} with agents...
Preprint
Full-text available
We design and develop a new shared Augmented Reality (AR) workspace for Human-Robot Interaction (HRI), which establishes a bi-directional communication between human agents and robots. In a prototype system, the shared AR workspace enables a shared perception, so that a physical robot not only perceives the virtual elements in its own view but also...
Preprint
Full-text available
We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used t...
Preprint
Full-text available
Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs. Specifically, a parse graph (pg) is learned from a single-view spatiotemporal parsing by...
Preprint
Full-text available
Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artific...
Article
Full-text available
Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artific...
Article
Full-text available
The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both fu...
Conference Paper
Full-text available
We propose VRGym, a virtual reality (VR) testbed for realistic human-robot interaction. Different from existing toolkits and VR environments, the VRGym emphasizes on building and training both physical and interactive agents for robotics, machine learning, and cognitive science. VRGym leverages mechanisms that can generate diverse 3D scenes with hi...
Conference Paper
Full-text available
This paper presents an incremental learning framework for mobile robots localizing the human sound source using a microphone array in a complex indoor environment consisting of multiple rooms. In contrast to conventional approaches that leverage direction-of-arrival (DOA) estimation, the framework allows a robot to accumulate training data and impr...
Conference Paper
Full-text available
This paper presents a design that jointly provides hand pose sensing, hand localization, and haptic feedback to facilitate real-time stable grasps in Virtual Reality (VR). The design is based on an easy-to-replicate glove-based system that can reliably perform (i) a high-fidelity hand pose sensing in real time through a network of 15 IMUs, and (ii)...
Preprint
Full-text available
We propose VRGym, a virtual reality testbed for realistic human-robot interaction. Different from existing toolkits and virtual reality environments, the VRGym emphasizes on building and training both physical and interactive agents for robotics, machine learning, and cognitive science. VRGym leverages mechanisms that can generate diverse 3D scenes...
Conference Paper
Full-text available
This paper presents a mirroring approach, inspired by the neuroscience discovery of the mirror neurons, to transfer demonstrated manipulation actions to robots. Designed to address the different embodiments between a human (demonstrator) and a robot, this approach extends the classic robot Learning from Demonstration (LfD) in the following aspects:...
Conference Paper
Full-text available
Contact forces of the hand are visually unobservable , but play a crucial role in understanding hand-object interactions. In this paper, we propose an unsupervised learning approach for manipulation event segmentation and manipulation event parsing. The proposed framework incorporates hand pose kinematics and contact forces using a low-cost easy-to...
Conference Paper
Full-text available
We present a novel Augmented Reality (AR) approach , through Microsoft HoloLens, to address the challenging problems of diagnosing, teaching, and patching interpretable knowledge of a robot. A Temporal And-Or graph (T-AOG) of opening bottles is learned from human demonstration and programmed to the robot. This representation yields a hierarchical s...
Conference Paper
Full-text available
We present a design of an easy-to-replicate glove-based system that can reliably perform simultaneous hand pose and force sensing in real time, for the purpose of collecting human hand data during fine manipulative actions. The design consists of a sensory glove that is capable of jointly collecting data of finger poses, hand poses, as well as forc...
Conference Paper
Full-text available
Learning complex robot manipulation policies for real-world objects is challenging, often requiring significant tuning within controlled environments. In this paper, we learn a manipulation model to execute tasks with multiple stages and variable structure, which typically are not suitable for most robot manipulation approaches. The model is learne...
Article
This paper presents a novel infrastructural traffic monitoring approach that estimates traffic information by combining two sensing techniques. The traffic information can be obtained from the presented approach includes passing vehicle counts, corresponding speed estimation and vehicle classification based on size. This approach uses measurement f...
Conference Paper
This paper presents a novel design of infrastructural traffic monitoring that performs vehicle counts, speed estimation, and vehicles classification by deploying three different approaches using two types of sensor, infrared (IR) cameras and laser range finders (LRFs). The first approach identifies passing vehicles by using LRFs and measuring the t...
Conference Paper
This paper presents an approach to the recursive Bayesian estimation of non-field-of-view (NFOV) sound source tracking based on reflection and diffraction signals with an incorporation of optical sensors. The approach takes multi-modal sensoy fusion of a mobile robot, which combines an optical 3D environment geometrical description with a microphon...

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Projects (2)
Project
Human-Robot Collaboration(Hu-Ro-Co)