Author name cluster

Hangxin Liu

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30 papers

2 author rows

IROS Conference 2025 Conference Paper

In-situ Value-aligned Human-Robot Interactions with Physical Constraints

Hongtao Li
Ziyuan Jiao
Xiaofeng Liu
Hangxin Liu
Zilong Zheng

Equipped with Large Language Models (LLMs), human-centered robots are now capable of performing a wide range of tasks that were previously deemed challenging or unattainable. However, merely completing tasks is insufficient for cognitive robots, who should learn and apply human preferences to future scenarios. In this work, we propose a framework that combines human preferences with physical constraints, requiring robots to complete tasks while considering both. Firstly, we developed a benchmark of everyday household activities, which are often evaluated based on specific preferences. We then introduced In-Context Learning from Human Feedback (ICLHF), where human feedback comes from direct instructions and adjustments made intentionally or unintentionally in daily life. Extensive sets of experiments, testing the ICLHF to generate task plans and balance physical constraints with preferences, have demonstrated the efficiency of our approach.

IROS Conference 2025 Conference Paper

R-Tac0: A Rounded High-Frequency Transferable Monochrome Vision-based Tactile Sensor for Shape Reconstruction

Wanlin Li
Pei Lin
Meng Wang 0051
Chenxi Xiao
Kaspar Althoefer
Yao Su 0001
Ziyuan Jiao
Hangxin Liu

Endowing the curved surfaces of rounded vision-based tactile fingers is essential for dexterous robotic manipulation, as they offer more sufficient contact with the environment. However, current rounded designs are constrained by a low sensing frequency (30–60 Hz) and the need for recalibration when adapting to new sensors due to the reliance on multi-channel captures, which hinders their performance in dynamic robotic tasks and large-scale deployment. In this work, we introduce R-Tac0, a low-cost rounded VBTS engineered for high-resolution and high-speed perception. The key innovation is a monochrome vision-based sensing principle: utilizing a black-and-white camera to capture the reflection properties of the compound rounded elastomer under monochromatic illumination. This single-channel imaging significantly reduces data volume and simplifies computational complexity, enabling 120 Hz tactile perception. A lightweight neural network can calibrate the sensor to achieve a depth reconstruction accuracy of 0. 169 mm per pixel, while exhibiting surprisingly good transferability to new sensors. In experiments, we demonstrate the advantages of R-Tac0’s rounded design by evaluating its performance under different contact angles, its high-frequency perception in slip detection, and its effectiveness in robotic dynamic pose estimation.

IROS Conference 2025 Conference Paper

SuperMag: Vision-based Tactile Data Guided High-resolution Tactile Shape Reconstruction for Magnetic Tactile Sensors

Peiyao Hou
Danning Sun
Meng Wang 0038
Yuzhe Huang
Zeyu Zhang 0001
Hangxin Liu
Wanlin Li
Ziyuan Jiao

Magnetic-based tactile sensors (MBTS) combine the advantages of compact design and high-frequency operation but suffer from limited spatial resolution due to their sparse taxel arrays. This paper proposes SuperMag, a tactile shape reconstruction method that addresses this limitation by leveraging high-resolution vision-based tactile sensor (VBTS) data to supervise MBTS super-resolution. Co-designed, open-source VBTS and MBTS with identical contact modules enable synchronized data collection of high-resolution shapes and magnetic signals via a symmetric calibration setup. We frame tactile shape reconstruction as a conditional generative problem, employing a conditional variational auto-encoder to infer high-resolution shapes from low-resolution MBTS inputs. The MBTS achieves a sampling frequency of 125 Hz, whereas the shape reconstruction sustains an inference time within 2. 5 ms. This cross-modality synergy advances tactile perception of the MBTS, potentially unlocking its new capabilities in high-precision robotic tasks.

ICRA Conference 2024 Conference Paper

ASPIRe: An Informative Trajectory Planner with Mutual Information Approximation for Target Search and Tracking

Kangjie Zhou
Pengying Wu
Yao Su 0001
Han Gao
Ji Ma 0007
Hangxin Liu
Chang Liu 0002

This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

IROS Conference 2024 Conference Paper

Driving Animatronic Robot Facial Expression From Speech

Boren Li
Hang Li
Hangxin Liu

Animatronic robots hold the promise of enabling natural human-robot interaction through lifelike facial expressions. However, generating realistic, speech-synchronized robot expressions poses significant challenges due to the complexities of facial biomechanics and the need for responsive motion synthesis. This paper introduces a novel, skinning-centric approach to drive animatronic robot facial expressions from speech input. At its core, the proposed approach employs linear blend skinning (LBS) as a unifying representation, guiding innovations in both embodiment design and motion synthesis. LBS informs the actuation topology, facilitates human expression retargeting, and enables efficient speech-driven facial motion generation. This approach demonstrates the capability to produce highly realistic facial expressions on an animatronic face in real-time at over 4000 fps on a single Nvidia RTX 4090, significantly advancing robots' ability to replicate nuanced human expressions for natural interaction. To foster further research and development in this field, the code has been made publicly available at: https://github.com/library87/OpenRoboExp.

IROS Conference 2024 Conference Paper

Flight Structure Optimization of Modular Reconfigurable UAVs

Yao Su 0001
Ziyuan Jiao
Zeyu Zhang 0001
Jingwen Zhang
Hang Li
Meng Wang 0051
Hangxin Liu

This paper presents a Genetic Algorithm (GA) designed to reconfigure a large group of modular Unmanned Aerial Vehicles (UAVs), each with different weights and inertia parameters, into an over-actuated flight structure with improved dynamic properties. Previous research efforts either utilized expert knowledge to design flight structures for a specific task or relied on enumeration-based algorithms that required extensive computation to find an optimal one. However, both approaches encounter challenges in accommodating the heterogeneity among modules. Our GA addresses these challenges by incorporating the complexities of over-actuation and dynamic properties into its formulation. Additionally, we employ a tree representation and a vector representation to describe flight structures, facilitating efficient crossover operations and fitness evaluations within the GA framework, respectively. Using cubic modular quadcopters capable of functioning as omnidirectional thrust generators, we validate that the proposed approach can (i) adeptly identify suboptimal configurations ensuring both over-actuation and trajectory tracking accuracy and (ii) significantly reduce computational costs compared to traditional enumeration-based methods.

IROS Conference 2024 Conference Paper

Large-scale Deployment of Vision-based Tactile Sensors on Multi-fingered Grippers

Meng Wang 0051
Wanlin Li
Hao Liang
Boren Li
Kaspar Althoefer
Yao Su 0001
Hangxin Liu

Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multi-surface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency, (ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.

IROS Conference 2024 Conference Paper

LLM 3: Large Language Model-based Task and Motion Planning with Motion Failure Reasoning

Shu Wang 0002
Muzhi Han
Ziyuan Jiao
Zeyu Zhang 0001
Ying Nian Wu
Song-Chun Zhu
Hangxin Liu

Conventional Task and Motion Planning (TAMP) approaches rely on manually designed interfaces connecting symbolic task planning with continuous motion generation. These domain-specific and labor-intensive modules are limited in addressing emerging tasks in real-world settings. Here, we present LLM 3, a novel Large Language Model (LLM)-based TAMP framework featuring a domain-independent interface. Specifically, we leverage the powerful reasoning and planning capabilities of pre-trained LLMs to propose symbolic action sequences and select continuous action parameters for motion planning. Crucially, LLM 3 incorporates motion planning feedback through prompting, allowing the LLM to iteratively refine its proposals by reasoning about motion failure. Consequently, LLM 3 interfaces between task planning and motion planning, alleviating the intricate design process of handling domain-specific messages between them. Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM 3 in solving TAMP problems and the efficiency in selecting action parameters. Ablation studies underscore the significant contribution of motion failure reasoning to the success of LLM 3. Furthermore, we conduct qualitative experiments on a physical manipulator, demonstrating the practical applicability of our approach in real-world settings.

ICRA Conference 2024 Conference Paper

Real-time Dynamic-consistent Motion Planning for Over-actuated UAVs

Yao Su 0001
Jingwen Zhang
Ziyuan Jiao
Hang Li
Meng Wang 0051
Hangxin Liu

Existing motion planning approaches for over-actuated unmanned aerial vehicle (UAV) platforms can achieve online planning without considering dynamics. However, in many envisioned application areas such as aerial manipulation, payload delivery, and moving target tracking, it is critical to ensure dynamic consistency in the generated trajectory. The dynamics of these platforms introduce a high nonlinearity, leading to a substantial increase in computational burden. This paper presents an efficient method to plan motions that are consistent with the dynamics of over-actuated UAVs. With a hierarchical control structure, the dimension of the optimization problem is greatly reduced with synthesized wrench commands. Additionally, by exploring the dynamics of over-actuated UAVs, the complex planning process is decoupled into two simpler sub-problems. As a result, the proposed planner can be solved as two small quadratic programmings (QPs) and deployed in real-time. The computational efficiency and dynamic consistency of the proposed method are verified through both simulations and experiments, including comparison with other approaches and dynamic target tracking.

IROS Conference 2023 Conference Paper

Aggregating Single-Wheeled Mobile Robots for Omnidirectional Movements

Meng Wang 0051
Yao Su 0001
Hang Li
Jiarui Li
Jixiang Liang
Hangxin Liu

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

IROS Conference 2023 Conference Paper

Learning a Causal Transition Model for Object Cutting

Zeyu Zhang 0001
Muzhi Han
Baoxiong Jia
Ziyuan Jiao
Yixin Zhu 0001
Song-Chun Zhu
Hangxin Liu

Cutting objects into desired fragments is challenging for robots due to the spatially unstructured nature of fragments and the complex one-to-many object fragmentation caused by actions. We present a novel approach to model object fragmentation using an attributed stochastic grammar. This grammar abstracts fragment states as node variables and captures causal transitions in object fragmentation through production rules. We devise a probabilistic framework to learn this grammar from human demonstrations. The planning process for object cutting involves inferring an optimal parse tree of desired fragments using the learned grammar, with parse tree productions corresponding to cutting actions. We employ Monte Carlo Tree Search (MCTS) to efficiently approximate the optimal parse tree and generate a sequence of executable cutting actions. The experiments demonstrate the efficacy in planning for object-cutting tasks, both in simulation and on a physical robot. The proposed approach outperforms several baselines by demonstrating superior generalization to novel setups, thanks to the compositionality of the grammar model.

IROS Conference 2023 Conference Paper

Part-level Scene Reconstruction Affords Robot Interaction

Zeyu Zhang 0001
Lexing Zhang
Zaijin Wang
Ziyuan Jiao
Muzhi Han
Yixin Zhu 0001
Song-Chun Zhu
Hangxin Liu

Existing methods for reconstructing interactive scenes primarily focus on replacing reconstructed objects with CAD models retrieved from a limited database, resulting in significant discrepancies between the reconstructed and observed scenes. To address this issue, our work introduces a part-level reconstruction approach that reassembles objects using primitive shapes. This enables us to precisely replicate the observed physical scenes and simulate robot interactions with both rigid and articulated objects. By segmenting reconstructed objects into semantic parts and aligning primitive shapes to these parts, we assemble them as CAD models while estimating kinematic relations, including parent-child contact relations, joint types, and parameters. Specifically, we derive the optimal primitive alignment by solving a series of optimization problems, and estimate kinematic relations based on part semantics and geometry. Our experiments demonstrate that part-level scene reconstruction outperforms object-level reconstruction by accurately capturing finer details and improving precision. These reconstructed part-level interactive scenes provide valuable kinematic information for various robotic applications; we showcase the feasibility of certifying mobile manipulation planning in these interactive scenes before executing tasks in the physical world.

ICRA Conference 2023 Conference Paper

Rearrange Indoor Scenes for Human-Robot Co-Activity

Weiqi Wang 0004
Zihang Zhao
Ziyuan Jiao
Yixin Zhu 0001
Song-Chun Zhu
Hangxin Liu

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot's accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide a robot with 14% more accessible space and 30% more objects to interact with on average. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.

IROS Conference 2023 Conference Paper

Sequential Manipulation Planning for Over-Actuated Unmanned Aerial Manipulators

Yao Su 0001
Jiarui Li
Ziyuan Jiao
Meng Wang 0051
Chi Chu
Hang Li
Yixin Zhu 0001
Hangxin Liu

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.

IROS Conference 2022 Conference Paper

Downwash-aware Control Allocation for Over-actuated UAV Platforms

Yao Su 0001
Chi Chu
Meng Wang 0051
Jiarui Li
Liu Yang
Yixin Zhu 0001
Hangxin Liu

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 of modeling aerodynamic airflow challenges control algorithms from properly compensating for such a side effect. Leveraging the input redundancies in over-actuated UAVs, we tackle this issue with a novel control allocation framework that considers downwash effects and explores the entire allocation space for an optimal solution. This optimal solution avoids downwash effects while providing high thrust efficiency within the hardware constraints. To the best of our knowledge, ours is the first formal derivation to investigate the downwash effects on over-actuated UAVs. We verify our framework on different hardware configurations in both simulation and experiment.

IROS Conference 2022 Conference Paper

Sequential Manipulation Planning on Scene Graph

Ziyuan Jiao
Yida Niu
Zeyu Zhang 0001
Song-Chun Zhu
Yixin Zhu 0001
Hangxin Liu

We devise a 3D scene graph representation, contact graph + (cg + ), for efficient sequential manipulation 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 produced by a genetic algorithm with a stochastic optimization method. A task plan is then initialized by computing the Graph Editing Distance (GED) between the initial contact graph and the goal configuration, which generates graph edit operations corresponding to possible robot actions. We finalize the task plan by imposing constraints to regulate the temporal feasibility of graph edit operations, ensuring valid task and motion correspondences. In a series of simulated and real experiments, robots successfully complete complex sequential object rearrangement tasks that are difficult to specify using conventional planning language like Planning Domain Definition Language (PDDL), demonstrating high potential of planning sequential manipulation tasks on cg +.

IROS Conference 2021 Conference Paper

Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations

Ziyuan Jiao
Zeyu Zhang 0001
Xin Jiang
David K. Han
Song-Chun Zhu
Yixin Zhu 0001
Hangxin Liu

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 by trajectory optimization. This new VKC perspective of mobile manipulation allows a service robot to (i) produce well-coordinated motions, suitable for complex household environments, and (ii) perform intricate multi-step tasks while interacting with multiple objects without an explicit definition of intermediate goals. In simulated experiments, we validate these advantages by comparing the VKC-based approach with baselines that solely optimize individual components. The results manifest that VKC-based joint modeling and planning promote task success rates and produce more efficient trajectories.

IROS Conference 2021 Conference Paper

Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective

Ziyuan Jiao
Zeyu Zhang 0001
Weiqi Wang 0004
David K. Han
Song-Chun Zhu
Yixin Zhu 0001
Hangxin Liu

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 unnecessary predicates in describing intermediate poses. As a result, these advantages simplify the design of the planning domain and significantly reduce the search space and branching factors in solving planning problems. In experiments, we implement a task planner using Planning Domain Definition Language (PDDL) with VKC. Compared with conventional domain definition, our VKC-based domain definition is more efficient in both planning time and memory. In addition, abstract actions perform better in producing feasible motion plans and trajectories. We further scale up the VKC-based task planner in complex mobile manipulation tasks. Taken together, these results demonstrate that task planning using VKC for mobile manipulation is not only natural and effective but also introduces new capabilities.

ICRA Conference 2021 Conference Paper

Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments

Muzhi Han
Zeyu Zhang 0001
Ziyuan Jiao
Xu Xie 0001
Yixin Zhu 0001
Song-Chun Zhu
Hangxin Liu

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 actionable information for simulating interactions with agents. Here, we address this challenging problem by reconstructing an interactive scene using RGB-D data stream, which captures (i) the semantics and geometry of objects and layouts by a 3D volumetric panoptic mapping module, and (ii) object affordance and contextual relations by reasoning over physical common sense among objects, organized by a graph-based scene representation. Crucially, this reconstructed scene replaces the object meshes in the dense panoptic map with part-based articulated CAD models for finer-grained robot interactions. In the experiments, we demonstrate that (i) our panoptic mapping module outperforms previous state-of-the-art methods, (ii) a high-performant physical reasoning procedure that matches, aligns, and replaces objects’ meshes with best-fitted CAD models, and (iii) reconstructed scenes are physically plausible and naturally afford actionable interactions; without any manual labeling, they are seamlessly imported to ROS-based simulators and virtual environments for complex robot task executions. 1

ICRA Conference 2020 Conference Paper

Congestion-aware Evacuation Routing using Augmented Reality Devices

Zeyu Zhang 0001
Hangxin Liu
Ziyuan Jiao
Yixin Zhu 0001
Song-Chun Zhu

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 to model the congestion distribution inside a building. To efficiently search the evacuation route among all destinations, a variant of A ⋆ algorithm is devised to obtain the optimal solution in a single pass. In a series of simulated studies, we show that the proposed algorithm is more computationally optimized compared to classic path planning algorithms; it generates a more time-efficient evacuation route for each individual that minimizes the overall congestion. A complete system using AR devices is implemented for a pilot study in real-world environments, demonstrating the efficacy of the proposed approach.

IROS Conference 2020 Conference Paper

Human-Robot Interaction in a Shared Augmented Reality Workspace

Shuwen Qiu
Hangxin Liu
Zeyu Zhang 0001
Yixin Zhu 0001
Song-Chun Zhu

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 infers the utility of the human agent-the cost needed to perceive and interact in AR-by sensing the human agent's gaze and pose. Such a new HRI design also affords a shared manipulation, wherein the physical robot can control and alter virtual objects in AR as an active agent; crucially, a robot can proactively interact with human agents, instead of purely passively executing received commands. In experiments, we design a resource collection game that qualitatively demonstrates how a robot perceives, processes, and manipulates in AR and quantitatively evaluates the efficacy of HRI using the shared AR workspace. We further discuss how the system can potentially benefit future HRI studies that are otherwise challenging.

ICRA Conference 2020 Conference Paper

Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs

Tao Yuan
Hangxin Liu
Lifeng Fan
Zilong Zheng
Tao Gao 0004
Yixin Zhu 0001
Song-Chun Zhu

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 aggregating various object states along the time; such a learned representation is accumulated as the robot’s knowledge. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning and inference capability to overcome the errors originated from a single view. In the experiments, through the joint inference over pgs, the system correctly recognizes human (false-)belief in various settings and achieves better cross-view accuracy on a challenging small object tracking dataset.

ICRA Conference 2019 Conference Paper

High-Fidelity Grasping in Virtual Reality using a Glove-based System

Hangxin Liu
Zhenliang Zhang 0002
Xu Xie 0001
Yixin Zhu 0001
Yue Liu 0005
Yongtian Wang
Song-Chun Zhu

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) the hand localization using a Vive Tracker. The supported physics-based simulation in VR is capable of detecting collisions and contact points for virtual object manipulation, which drives the collision event to trigger the physical vibration motors on the glove to signal the user, providing a better realism inside virtual environments. A caging-based approach using collision geometry is integrated to determine whether a grasp is stable. In the experiment, we showcase successful grasps of virtual objects with large geometry variations. Comparing to the popular LeapMotion sensor, we demonstrate the proposed glove-based design yields a higher success rate in various tasks in VR. We hope such a glove-based system can simplify the data collection of human manipulations with VR.

AAAI Conference 2019 Conference Paper

Mirroring without Overimitation: Learning Functionally Equivalent Manipulation Actions

Hangxin Liu
Chi Zhang
Yixin Zhu
Chenfanfu Jiang
Song-Chun Zhu

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: i) It incorporates fine-grained hand forces collected by a tactile glove in demonstration to learn robot’s fine manipulative actions; ii) Through model-free reinforcement learning and grammar induction, the demonstration is represented by a goal-oriented grammar consisting of goal states and the corresponding forces to reach the states, independent of robot embodiments; iii) A physics-based simulation engine is applied to emulate various robot actions and mirrors the actions that are functionally equivalent to the human’s in the sense of causing the same state changes by exerting similar forces. Through this approach, a robot reasons about which forces to exert and what goals to achieve to generate actions (i. e. , mirroring), rather than strictly mimicking demonstration (i. e. , overimitation). Thus the embodiment difference between a human and a robot is naturally overcome. In the experiment, we demonstrate the proposed approach by teaching a real Baxter robot with a complex manipulation task involving haptic feedback—opening medicine bottles.

ICRA Conference 2019 Conference Paper

Self-Supervised Incremental Learning for Sound Source Localization in Complex Indoor Environment

Hangxin Liu
Zeyu Zhang 0001
Yixin Zhu 0001
Song-Chun Zhu

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 improve the performance of the prediction model over time using an incremental learning scheme. Specifically, we use implicit acoustic features obtained from an auto-encoder together with the geometry features from the map for training. A self-supervision process is developed such that the model ranks the priority of rooms to explore and assigns the ground truth label to the collected data, updating the learned model on-the-fly. The framework does not require pre-collected data and can be directly applied to real-world scenarios without any human supervisions or interventions. In experiments, we demonstrate that the prediction accuracy reaches 67% using about 20 training samples and eventually achieves 90% accuracy within 120 samples, surpassing prior classification-based methods with explicit GCC-PHAT features.

ICRA Conference 2018 Conference Paper

Interactive Robot Knowledge Patching Using Augmented Reality

Hangxin Liu
Yaofang Zhang
Wenwen Si
Xu Xie 0001
Yixin Zhu 0001
Song-Chun Zhu

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 structure that captures the compositional nature of the given task, which is highly interpretable for the users. By visualizing the knowledge structure represented by a T-AOG and the decision making process by parsing the T-AOG, the user can intuitively understand what the robot knows, supervise the robot's action planner, and monitor visually latent robot states ( e. g. , the force exerted during interactions). Given a new task, through such comprehensive visualizations of robot's inner functioning, users can quickly identify the reasons of failures, interactively teach the robot with a new action, and patch it to the current knowledge structure. In this way, the robot is capable of solving similar but new tasks only through minor modifications provided by the users interactively. This process demonstrates the interpretability of our knowledge representation and the effectiveness of the AR interface.

ICRA Conference 2018 Conference Paper

Unsupervised Learning of Hierarchical Models for Hand-Object Interactions

Xu Xie 0001
Hangxin Liu
Mark Edmonds
Feng Gao 0013
Siyuan Qi
Yixin Zhu 0001
Brandon Rothrock
Song-Chun Zhu

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-replicate tactile glove. We use a temporal grammar model to capture the hierarchical structure of events, integrating extracted force vectors from the raw sensory input of poses and forces. The temporal grammar is represented as a temporal And-Or graph (T-AOG), which can be induced in an unsupervised manner. We obtain the event labeling sequences by measuring the similarity between segments using the Dynamic Time Alignment Kernel (DTAK). Experimental results show that our method achieves high accuracy in manipulation event segmentation, recognition and parsing by utilizing both pose and force data.

IROS Conference 2017 Conference Paper

A glove-based system for studying hand-object manipulation via joint pose and force sensing

Hangxin Liu
Xu Xie 0001
Matt Millar
Mark Edmonds
Feng Gao 0013
Yixin Zhu 0001
Veronica J. Santos
Brandon Rothrock

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 forces on palm and each phalanx. Specifically, the sensory glove employs a network of 15 IMUs to measure the rotations between individual phalanxes. Hand pose is then reconstructed using forward kinematics. Contact forces on the palm and each phalanx are measured by 6 customized force sensors made from Velostat, a piezoresistive material whose force-voltage relation is investigated. We further develop an open-source software pipeline consisting of drivers and processing code and a system for visualizing hand actions that is compatible with the popular Raspberry Pi architecture. In our experiment, we conduct a series of evaluations that quantitatively characterize both individual sensors and the overall system, proving the effectiveness of the proposed design.

IROS Conference 2017 Conference Paper

Feeling the force: Integrating force and pose for fluent discovery through imitation learning to open medicine bottles

Mark Edmonds
Feng Gao 0013
Xu Xie 0001
Hangxin Liu
Siyuan Qi
Yixin Zhu 0001
Brandon Rothrock
Song-Chun Zhu

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 learned from human demonstration using a tactile glove that measures both hand pose and contact forces. The tactile glove enables observation of visually latent changes in the scene, specifically the forces imposed to unlock the child-safety mechanisms of medicine bottles. From these observations, we learn an action planner through both a top-down stochastic grammar model (And-Or graph) to represent the compositional nature of the task sequence and a bottom-up discriminative model from the observed poses and forces. These two terms are combined during planning to select the next optimal action. We present a method for transferring this human-specific knowledge onto a robot platform and demonstrate that the robot can perform successful manipulations of unseen objects with similar task structure.

IROS Conference 2016 Conference Paper

Non-field-of-view sound source localization using diffraction and reflection signals

Kuya Takami
Hangxin Liu
Tomonari Furukawa
Makoto Kumon
Gamini Dissanayake

This paper describes a non-field-of-view (NFOV) localization approach for a mobile robot in an unknown environment based on an acoustic signal combined with the geometrical information from an optical sensor. The approach estimates the location of a target through the mobile robot's sensor observation frame, which consists of a combination of diffraction and reflection acoustic signals and a 3-D environment geometrical description. This fusion of audio-visual sensor observation likelihoods allows the robot to estimate the NFOV target. The diffraction and reflection observations from the microphone array generate the acoustic joint observation likelihood. The observed geometry also determines far-field or near-field acoustic conditions to improve the estimation of the sound direction of arrival. A mobile robot equipped with a microphone array and an RGB-D sensor was tested in a controlled environment, an anechoic chamber, to demonstrate the NFOV localization capabilities. This resulted in +/- 18 degrees, and less than 0. 75 m error in angle and distance estimation, respectively.