Author name cluster

Yu Sun 0004

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

29 papers

1 author row

ICRA Conference 2024 Conference Paper

From Cooking Recipes to Robot Task Trees - Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network

Md Sadman Sakib
Yu Sun 0004

Task planning for robotic cooking involves generating a sequence of actions for a robot to prepare a meal successfully. This paper introduces a novel task tree generation pipeline producing correct planning and efficient execution for cooking tasks. Our method first uses a large language model (LLM) to retrieve recipe instructions and then utilizes a fine-tuned GPT-3 to convert them into a task tree, capturing sequential and parallel dependencies among subtasks. The pipeline then mitigates the uncertainty and unreliable features of LLM outputs using task tree retrieval. We combine multiple LLM task tree outputs into a graph and perform a task tree retrieval to avoid questionable nodes and high-cost nodes to improve planning correctness and execution efficiency. Our evaluation results show its superior performance in task planning accuracy and efficiency compared to previous works.

ICLR Conference 2024 Conference Paper

Tool-Augmented Reward Modeling

Lei Li 0040
Yekun Chai
Shuohuan Wang
Yu Sun 0004
Hao Tian 0005
Ningyu Zhang 0001
Hua Wu 0003

Reward modeling (*a.k.a.*, preference modeling) is instrumental for aligning large language models with human preferences, particularly within the context of reinforcement learning from human feedback (RLHF). While conventional reward models (RMs) have exhibited remarkable scalability, they oft struggle with fundamental functionality such as arithmetic computation, code execution, and factual lookup. In this paper, we propose a tool-augmented preference modeling approach, named Themis, to address these limitations by empowering RMs with access to external environments, including calculators and search engines. This approach not only fosters synergy between tool utilization and reward grading but also enhances interpretive capacity and scoring reliability. Our study delves into the integration of external tools into RMs, enabling them to interact with diverse external sources and construct task-specific tool engagement and reasoning traces in an autoregressive manner. We validate our approach across a wide range of domains, incorporating seven distinct external tools. Our experimental results demonstrate a noteworthy overall improvement of 17.7% across eight tasks in preference ranking. Furthermore, our approach outperforms Gopher 280B by 7.3% on TruthfulQA task in zero-shot evaluation. In human evaluations, RLHF trained with Themis attains an average win rate of 32% when compared to baselines across four distinct tasks. Additionally, we provide a comprehensive collection of tool-related RM datasets, incorporating data from seven distinct tool APIs, totaling 15,000 instances. We have made the code, data, and model checkpoints publicly available to facilitate and inspire further research advancements (https://github.com/ernie-research/Tool-Augmented-Reward-Model).

IROS Conference 2022 Conference Paper

Multi-Object Grasping - Efficient Robotic Picking and Transferring Policy for Batch Picking

Adheesh Shenoy
Tianze Chen
Yu Sun 0004

In a typical fulfillment center, the order fulfilling process is managed by a warehouse management system (WMS). For efficiency, WMS usually applies batch picking, also called multi-order picking, to collect the same items for multiple orders. Suppose an item appears in multiple orders, instead of repeatedly revisiting the exact picking location multiple times, a picker will be instructed to pick up multiple same items at once and bring them to a sorting station, also called a re-bin station. It is at the re-bin station, where the workers sort the picked items into separate orders. We have seen many robotic technologies being developed for sorting. However, we have not seen any feasible robotic technology for batch picking. Transferring multiple objects between bins is a common task. In robotics, a standard approach is to transfer a single object at a time. However, grasping multiple objects and transferring them at once is more efficient. This paper presents a set of novel strategies for efficiently grasping and transferring multiple objects. The grasping strategies enable a robotic hand to grasp multiple objects by identifying an optimal ready hand configuration (pre-grasp), calculating a flexion synergy based on the desired quantity of objects to be grasped, and utilizing a deep learning model to signal the completion of a grasp. The transferring strategies demonstrate an approach that models the problem as a Markov decision process (MDP) and defines specific grasping actions to efficiently transfer objects when the required quantity is larger than the capability of a single grasp. Using the MDP model, the approach can generate an optimal pick-transfer policy that minimizes the number of transfers. The complete proposed approach has been evaluated in both a simulation environment and on a real robotic system. The proposed approach reduces the number of transfers by 59% and the number of lifts by 58% compared to an optimal single object pick-transfer solution.

ICRA Conference 2022 Conference Paper

Multi-Object Grasping - Types and Taxonomy

Yu Sun 0004
Eliza Amatova
Tianze Chen

This paper proposes 12 multi-object grasps (MOGs) types from a human and robot grasping data set. The grasp types are then analyzed and organized into a MOG taxonomy. This paper first presents three MOG data collection setups: a human finger tracking setup for multi-object grasping demonstrations, a real system with Barretthand, UR5e arm, and a MOG algorithm, a simulation system with the same settings as the real system. Then the paper describes a novel stochastic grasping routine designed based on a biased random walk to explore the robotic hand's configuration space for feasible MOGs. Based on obser-vations in both the human demonstrations and robotic MOG solutions, this paper proposes 12 MOG types in two groups: shape-based types and function-based types. The new MOG types are compared using six characteristics and then compiled into a taxonomy. This paper then introduces the observed MOG type combinations and shows examples of 16 different combinations.

IROS Conference 2021 Conference Paper

Learning State-Dependent Sensor Measurement Models with Limited Sensor Measurements

Troi Williams
Yu Sun 0004

We present a two-stage transfer learning method for training state-dependent sensor measurement models (SDSMMs) with limited sensor data. This method can alleviate collecting sizeable sensor and ground truth data to learn accurate sensor models, especially when we must learn many sensor models (for example, a fleet of autonomous cars, drones, or warehouse robots). In the first stage, we use prior knowledge of the sensor (such as a physical model) to generate a sizeable artificial dataset. Then the artificial dataset is used to pre-train an SDSMM. The second stage fine-tunes the pre-trained SDSMM using a "small" number of data collected by our target real sensor. To our knowledge, we are the first to learn measurement distributions using data generated from a physical model and data from a real sensor. We evaluated our proposed method using the Extended Kalman Particle Filter and a real-world localization dataset collected by several robots. Compared to the prior method, the proposed method achieved comparable performance with as little as ~19% of the real training data.

IROS Conference 2021 Conference Paper

Multi-Object Grasping - Estimating the Number of Objects in a Robotic Grasp

Tianze Chen
Adheesh Shenoy
Anzhelika Kolinko
Syed Shah
Yu Sun 0004

A human hand can grasp a desired number of objects at once from a pile based solely on tactile sensing. To do so, a robot needs to make a grasp in a pile, sense the number of objects in the grasp before lifting, and predict how many will remain in the grasp after lifting. It is a very challenging problem because when making the prediction, the robotic hand is still in the pile and the objects in the grasp are not observable to vision systems. Moreover, some objects in the hand before lifting may fall out the grasp when the lifting starts because they were supported by other objects in the pile instead of the fingers. A robotic hand should sense how many objects are in a grasp using its tactile sensors before lifting. This paper presents novel multi-object grasping analyzing methods to solve this problem. They include a grasp volume calculation, tactile force analysis, and a data-driven deep learning approach. The methods have been implemented on a Barrett hand and then evaluated in simulations and a real setup with a robotic system. The evaluation results conclude that once the Barrett hand grasps multiple objects in the pile, the data-driven models can make a good prediction before lifting on how many objects will remain in the hand after lifting. The root-mean-square errors are 0. 74 for balls and 0. 58 for cubes in simulations, and 1. 06 for balls and 1. 45 for cubes in the real system.

ICRA Conference 2021 Conference Paper

Task Planning with a Weighted Functional Object-Oriented Network

David Paulius
Kelvin Sheng Pei Dong
Yu Sun 0004

In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our functional object-oriented network (FOON) that integrates weights in its functional units to reflect a robot’s chance of successfully executing an action of that functional unit. The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort. Through a number of experiments, this paper shows several successful cases in which using the proposed weighted FOON and the task planning algorithm allow a robot and a human to successfully complete complicated tasks together with higher success rates than a robot doing them alone.

IROS Conference 2020 Conference Paper

Estimating Motion Codes from Demonstration Videos

Maxat Alibayev
David Paulius
Yu Sun 0004

A motion taxonomy can encode manipulations as a binary-encoded representation, which we refer to as motion codes. These motion codes innately represent a manipulation action in an embedded space that describes the motion’s mechanical features, including contact and trajectory type. The key advantage of using motion codes for embedding is that motions can be more appropriately defined with robotic-relevant features, and their distances can be more reasonably measured using these motion features. In this paper, we develop a deep learning pipeline to extract motion codes from demonstration videos in an unsupervised manner so that knowledge from these videos can be properly represented and used for robots. Our evaluations show that motion codes can be extracted from demonstrations of action in the EPIC-KITCHENS dataset.

IROS Conference 2020 Conference Paper

Generalizing Learned Manipulation Skills in Practice

Juan Wilches
Yongqiang Huang 0001
Yu Sun 0004

Robots should be able to learn and perform a manipulation task across different settings. This paper presents an approach that learns an RNN-based manipulation skill model from demonstrations and then generalizes the learned skill in new settings. The manipulation skill model learned from demonstrations in an initial set of setting performs well in those settings and similar ones. However, the model may perform poorly in a novel setting that is significantly different from the learned settings. Therefore a novel approach called generalization in practice (GiP) is developed to tackle this critical problem. In this approach, the robot practices in the new setting to obtain new training data and refine the learned skill using the new data to gradually improve the learned skill model. The proposed approach has been implemented for one type of manipulation task – pouring that is the most performed manipulation in cooking applications. The presented approach enables a pouring robot to pour gracefully like a person in terms of speed and accuracy in learned setups and gradually improve the pouring performance in novel setups after several practices.

IROS Conference 2019 Conference Paper

Accurate Pouring using Model Predictive Control Enabled by Recurrent Neural Network

Tianze Chen
Yongqiang Huang 0001
Yu Sun 0004

Humans perform the task of pouring often and in which exhibit consistent accuracy regardless of the complicated dynamics of the liquid. Model predictive control (MPC) appears to be a natural candidate solution for the task of accurate pouring considering its wide use in industrial applications. However, MPC requires the model of the system in question. Since an accurate model of the liquid dynamics is difficult to obtain, the usefulness of MPC for the pouring task is uncertain. In this work, we model the dynamics of water using a recurrent neural network (RNN), which enables the use of MPC for pouring control. We evaluated our RNN-enabled MPC controller using a physical system we made ourselves and averaged a pouring error of 16. 4mL over 5 different source containers. We also compared our controller with a baseline switch controller and showed that our controller achieved a much higher accuracy than the baseline controller.

IROS Conference 2019 Conference Paper

Learning State-Dependent, Sensor Measurement Models for Localization

Troi Williams
Yu Sun 0004

A robot typically relies on sensor measurements to infer its state and the state of its environment. Unfortunately, sensor measurements are noisy, and the amount of noise can vary with state. The literature provides a collection of methods that estimate and adapt measurement noise over time. However, many methods do not assume that measurement noise is stochastic, or they do not estimate sensor measurement bias and noise based on state. In this paper, we propose a novel method called state-dependent, sensor measurement models(SDSMMs). This method: 1) learns to estimate measurement probability density functions directly from sensor measurements and 2) stochastically estimates an expected measurement (which includes measurement bias) and a measurement noise, both of which are conditioned upon the states of a robot and its environment. Throughout this paper, we discuss how to learn an SDSMM and use it with the Extended Kalman Filter (EKF). We then apply our method to solve an EKF localization problem using a real robot dataset. Our localization results showed that at least one of our proposed methods outperformed a standard EKF in all 15 cases for 2D position error and 10 of 15 cases for 1D orientation error. Our methods had a mean improvement of 39% for position and 15% for orientation.

IROS Conference 2019 Conference Paper

Manipulation Motion Taxonomy and Coding for Robots

David Paulius
Yongqiang Huang 0001
Jason Meloncon
Yu Sun 0004

This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations. Using instructional videos as a reference, we selected a list of common manipulation motions seen in cooking activities grouped into similar motions based on several trajectory and contact attributes. Manipulation codes are then developed based on the taxonomy attributes to represent the manipulation motions. The manipulation taxonomy is then used for comparing motion data in the Daily Interactive Manipulation (DIM) data set to reveal their motion similarities.

ICRA Conference 2018 Conference Paper

Functional Object-Oriented Network: Construction & Expansion

David Paulius
Ahmad Babaeian Jelodar
Yu Sun 0004

We build upon the functional object-oriented network (FOON), a structured knowledge representation which is constructed from observations of human activities and manipulations. A FOON can be used for representing object-motion affordances. Knowledge retrieval through graph search allows us to obtain novel manipulation sequences using knowledge spanning across many video sources, hence the novelty in our approach. However, we are limited to the sources collected. To further improve the performance of knowledge retrieval as a follow up to our previous work, we discuss generalizing knowledge to be applied to objects which are similar to what we have in FOON without manually annotating new sources of knowledge. We discuss two means of generalization: 1) expanding our network through the use of object similarity to create new functional units from those we already have, and 2) compressing the functional units by object categories rather than specific objects. We discuss experiments which compare the performance of our knowledge retrieval algorithm with both expansion and compression by categories.

IROS Conference 2017 Conference Paper

Learning to pour

Yongqiang Huang 0001
Yu Sun 0004

Pouring is a simple task people perform daily. It is the second most frequently executed motion in cooking scenarios, after pick-and-place. We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring. The approach uses recurrent neural networks as its building blocks. We collected the pouring demonstrations which we used for training. To test our approach in simulation, we also created and trained a force estimation system. The simulated experiments show that the system is able to generalize to single unseen element of the pouring characteristics.

IROS Conference 2016 Conference Paper

Functional object-oriented network for manipulation learning

David Paulius
Yongqiang Huang 0001
Roger Milton
William D. Buchanan
Jeanine Sam
Yu Sun 0004

This paper presents a novel structured knowledge representation called the functional object-oriented network (FOON) to model the connectivity of the functional-related objects and their motions in manipulation tasks. The graphical model FOON is learned by observing object state change and human manipulations with the objects. Using a well-trained FOON, robots can decipher a task goal, seek the correct objects at the desired states on which to operate, and generate a sequence of proper manipulation motions. The paper describes FOON's structure and an approach to form a universal FOON with extracted knowledge from online instructional videos. A graph retrieval approach is presented to generate manipulation motion sequences from the FOON to achieve a desired goal, demonstrating the flexibility of FOON in creating a novel and adaptive means of solving a problem using knowledge gathered from multiple sources. The results are demonstrated in a simulated environment to illustrate the motion sequences generated from the FOON to carry out the desired tasks.

IROS Conference 2015 Conference Paper

Generating manipulation trajectory using motion harmonics

Yongqiang Huang 0001
Yu Sun 0004

This paper presents a novel manipulation trajectory generating algorithm that constructs trajectories from learned motion harmonics and user defined constraints. The algorithm uses functional eigenanalysis to learn motion harmonics from demonstrated motions and then use the motion harmonics to compute the optimal trajectory that resembles the demonstrated motions and also satisfies the constraints. The algorithm has been tested on five real human motion data sets to obtain motion harmonics and then generate motions of each task for a NAO robot. The generated trajectories were compared with the trajectories generated using linear segment with parabolic blend approach and with the Open Motion Planning Library. The approach can also work with motion planners.

IROS Conference 2015 Conference Paper

Task-based grasp quality measures for grasp synthesis

Yun Lin 0003
Yu Sun 0004

To facilitate manipulation tasks, grasp should be selected intelligently to fulfill different stability properties and manipulative requirements in the tasks. In this paper, two task-dependent grasp quality measures are introduced: task wrench coverage measure and the manipulator efficiency measure. The first one measures the ability of a grasp to provide required interactive wrench during a task, while the second measures the effort that the manipulator takes for the whole manipulation process in facilitating the required instrument motion, which is determined by the grasp when the motion of the instrument is defined. The proposed measures are then used in selecting grasps for three typical manipulation tasks in simulations and using a real robotic system and produced successful grasp synthesis outcomes that satisfy manipulative requirements.

IROS Conference 2014 Conference Paper

Grasp planning based on strategy extracted from demonstration

Yun Lin 0003
Yu Sun 0004

In this paper, we discuss information that is beneficial to robotic grasp planning and can be extracted from human demonstration. We present a method that integrates grasp intention: grasp type, and the relative thumb positions and orientations on the grasped object to the force-closure-based grasp planning procedure. Instead of completely mimicking the human grasp, grasp type and the relative thumb position are partially extracted from the demonstration to represent the task properties and grasp strategies, and avoid the challenging kinematic correspondence problem. Instead of mapping the demonstrated motion, the grasp type and thumb position provide meaningful constraints on hand posture and wrist position. Both the feasible workspace of a robotic hand and the search space of grasp planning are thereby highly reduced by the constraints. This approach has been evaluated in a simulation with a Barrett hand and a Shadow hand on eight daily objects.

IROS Conference 2013 Conference Paper

Functional analysis of grasping motion

Wei Dai
Yu Sun 0004
Xiaoning Qian

This paper presents a novel grasping motion analysis technique based on functional principal component analysis (fPCA). The functional analysis of grasping motion provides an effective representation of grasping motion and emphasizes motion dynamic features that are omitted by classic PCA-based approaches. The proposed approach represents, processes, and compares grasping motion trajectories in a low-dimensional space. An experiment was conducted to record grasping motion trajectories of 15 different grasp types in Cutkosky grasp taxonomy. We implemented our method for the analysis of collected grasping motion in the PCA+fPCA space, which generated a new data-driven taxonomy of the grasp types, and naturally clustered grasping motion into 5 consistent groups across 5 different subjects. The robustness of the grouping was evaluated and confirmed using a tenfold cross validation approach.

ICRA Conference 2013 Conference Paper

Grasp mapping using locality preserving projections and kNN regression

Yun Lin 0003
Yu Sun 0004

In this paper, we propose a novel mapping approach to map a human grasp to a robotic grasp based on human grasp motion trajectories rather than grasp poses, since the grasp trajectories of a human grasp provide more information to disambiguate between different grasp types than grasp poses. Human grasp motions usually contain complex and nonlinear patterns in a high-dimensional space. In this paper, we reduced the high-dimensionality of motion trajectories by using locality preserving projections (LPP). Then, a Hausdorff distance was performed to find the k-nearest neighbor trajectories in the reduced low-dimensional subspace, and k-nearest neighbor (kNN) regression was used to map a demonstrated grasp motion by a human hand to a robotic hand. Several experiments were designed and carried out to compare the robotic grasping trajectory generated with and without the trajectory-based mapping approach. The regression errors of the mapping results show that our approach generates more robust grasps than using only grasp poses. In addition, our approach has the ability to successfully map a grasp motion of a new grasp demonstration that has not been trained before to a robotic hand.

ICRA Conference 2012 Conference Paper

Learning grasping force from demonstration

Yun Lin 0003
Shaogang Ren
Matthew Clevenger
Yu Sun 0004

This paper presents a novel force learning framework to learn fingertip force for a grasping and manipulation process from a human teacher with a force imaging approach. A demonstration station is designed to measure fingertip force without attaching force sensor on fingertips or objects so that this approach can be used with daily living objects. A Gaussian Mixture Model (GMM) based machine learning approach is applied on the fingertip force and position to obtain the motion and force model. Then a force and motion trajectory is generated with Gaussian Mixture Regression (GMR) from the learning result. The force and motion trajectory is applied to a robotic arm and hand to carry out a grasping and manipulation task. An experiment was designed and carried out to verify the learning framework by teaching a Fanuc robotic arm and a BarrettHand a pick-and-place task with demonstration. Experimental results show that the robot applied proper motions and forces in the pick-and-place task from the learned model.

ICRA Conference 2012 Conference Paper

MARVEL: A wireless Miniature Anchored Robotic Videoscope for Expedited Laparoscopy

Cristian A. Castro
Sara Smith
Adham Alqassis
Thomas Ketterl
Yu Sun 0004
Sharona Ross
Alexander Rosemurgy
Peter P. Savage

This paper describes the design and implementation of a Miniature Anchored Robotic Videoscope for Expedited Laparoscopy (MARVEL) and Camera Module (CM) that features wireless communications and control. The CM decreases the surgical-tool bottleneck experienced by surgeons in state-of-the art Laparoscopic Endoscopic Single-Site (LESS) procedures for minimally invasive abdominal surgery. The system includes: (1) a near-zero latency video wireless communications link, (2) a pan/tilt camera platform, actuated by two motors that provides surgeons a full hemisphere field of view inside the abdominal cavity, (3) a small wireless camera, (4) a wireless illumination control system, and (5) a wireless human-machine interface (HMI) to control the CM. An in-vivo experiment on a porcine subject was carried out to test the performance of the system. The robotic design is a Research Platform for a broad range of experiments in a range of domains for faculty and students in the Colleges of Engineering and Medicine and at Tampa General Hospital. This research is the first step in developing semi-autonomous wirelessly controlled and networked laparoscopic devices to enable a paradigm shift in minimally invasive surgery and other domains such as Wireless Body Area Networks.

ICRA Conference 2012 Conference Paper

Visual servoing control of a 9-DoF WMRA to perform ADL tasks

William G. Pence
Fabian Farelo
Redwan Alqasemi
Yu Sun 0004
Rajiv V. Dubey

The wheelchair-mounted robotic arm (WMRA) is a mobile manipulator that consists of a 7-DoF robotic arm and a 2-DoF power wheelchair platform. Previous works combined mobility and manipulation control using weighted optimization for dual-trajectory tracking [7]. In this work, we present an image-based visual servoing (IBVS) approach with scale-invariant feature transform (SIFT) using an eye-in-hand monocular camera for combined control of mobility and manipulation for the 9-DoF WMRA system to execute activities of daily living (ADL) autonomously. We also present results of the physical implementation with a simple “Go to and Pick Up” task and the “Go to and Open the Door” task previously published in simulation, using IBVS to aid the task performance.

ICRA Conference 2011 Conference Paper

5-D force control system for fingernail imaging calibration

Yun Lin 0003
Yu Sun 0004

This paper presents a low-cost automated system that is able to apply a 5-degree-of-freedom (DOF) force on a human fingertip with high precision. It is designed to be used as a calibration platform for the previous proposed fingernail imaging system, and as a haptic system. The system is composed of two Novint Falcon devices linked by two universal joints and a rigid bar to provide 5-DOF motion and force, with feedback from a 6-DOF force sensor. A force controller is designed with an inner position control to meet the calibration goal and requirement. Experiment result and analysis showed that the system was capable of controlling the force with a settling time of less than 0. 25 seconds. Two force trajectories are designed for fast and sufficient calibrations. A calibration experiments demonstrated that the system tracked the trajectories with an interval of 0. 3 seconds, and step sizes of 0. 1 N and 1 N·mm with root-mean-squared errors of 0. 02 – 0. 04 N for forces and 0. 39 N·mm for torque.

ICRA Conference 2011 Conference Paper

Fingertip force and contact position and orientation sensor

Yu Sun 0004

This paper presents a novel integrated system that is composed of a fingerprint sensor and a force sensor to measure contact position and orientation of the fingertip along with the contact force. The system uses fingerprints from the fingerprint sensor to identify the contact position and orientation with fingerprint features such as core point and ridge orientations. The contact position and orientation are represented in a fingerpad coordinate system for grasping studies. An experiment has been designed to evaluate the proposed system in terms of accuracy and resolution with three subjects. The proposed system can be used in human grasping studies to characterize the fingerpad contact.

ICRA Conference 2008 Conference Paper

Active robot calibration algorithm

Yu Sun 0004
John M. Hollerbach

This paper presents a new updating algorithm to reduce the complexity of computing an observability index for kinematic calibration of robots. An active calibration algorithm is developed to include an updating algorithm in the pose selection process. Simulations on a 6-DOF PUMA robot with 27 unknown parameters shows that the proposed algorithm performs more than 50, 000 times better than exhaustive search based on randomly generated designs.

ICRA Conference 2008 Conference Paper

Observability index selection for robot calibration

Yu Sun 0004
John M. Hollerbach

This paper relates 5 observability indexes for robot calibration to the "alphabet optimalities" from the experimental design literature. These 5 observability indexes are shown to be the upper and lower bounds of one another. All observability indexes are proved to be equivalent when the design is optimal after a perfect column scaling. It is shown that when the goal is to minimize the variance of the parameters, D-optimality is the best criterion. When the goal is to minimize the uncertainty of the end-effector position, E-optimality is the best criterion. It is proved that G-optimality is equivalent to E-optimality for exact design.

ICRA Conference 2007 Conference Paper

EigenNail for Finger Force Direction Recognition

Yu Sun 0004
John M. Hollerbach
Stephen A. Mascaro

This paper presents a technique termed EigenNails to classify fingertip force during contact based on the coloration patterns in the fingernail and surrounding skin. Fingertip force is classified into six directions: no force, normal force only, two directions (left/right) of lateral shear force, and two directions (forward/backward) of longitudinal shear forces. Based on the face recognition technique Eigenfaces, a small number of EigenNails are sufficient to express the color pattern features for shear force direction classification. Results show that 98% of 960 fingernail images of 8 different subjects are correctly classified. The lowest imaging resolution without sacrificing classification accuracy is found to be 10-by-10.

ICRA Conference 2006 Conference Paper

Dynamic Features and Prediction Model for Imaging the Fingernail to Measure Fingertip Forces

Yu Sun 0004
John M. Hollerbach
Stephen A. Mascaro

As an extension of our previous work on estimating fingertip forces by imaging the fingernail (Y. Sun, et al. , 2006), the dynamic features of the coloration response of different parts of the fingernail and surrounding skin to different force levels are studied. The effect of the cardiovascular state on measurable coloration is also characterized. The accuracy of normal force estimated by generalized least squares is presented. A time compensation method for fast force estimation is presented, based on a knowledge of the time constants from the dynamic response of individual fingernail regions