Knowledge-Augmented Dexterous Grasping with Incomplete Sensing

11/17/2020 ∙ by Bharath Rao, et al. ∙ Wichita State University 0

Humans can determine a proper strategy to grasp an object according to the measured physical attributes or the prior knowledge of the object. This paper proposes an approach to determining the strategy of dexterous grasping by using an anthropomorphic robotic hand simply based on a label or a description of an object. Object attributes are parsed from natural-language descriptions and augmented with an object knowledge base that is scraped from retailer websites. A novel metric named joint probability distance is defined to measure distance between object attributes. The probability distribution of grasp types for the given object is learned using a deep neural network which takes in object features as input. The action of the multi-fingered hand with redundant degrees of freedom (DoF) is controlled by a linear inverse-kinematics model of grasp topology and scales. The grasping strategy generated by the proposed approach is evaluated both by simulation and execution on a Sawyer robot with an AR10 robotic hand.

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I Introduction

Dexterous grasping is critical for complex assembly and delicate tool handling in industrial automation and advanced manufacturing [li2016dexterous]. Dexterous robotic manipulation replies on comprehensive and precise measurement of the work context [hui20magic], which is usually impractical and expensive for industrial applications. It is even challenging to measure important parameters of objects for grasping in an online mode, such as 3D dimensions, materials, and weights. It is therefore beneficial to research an approach to plan dexterous grasping without complete or accurate sensing of object characteristics.

A five-digit hand configuration with an opposable thumb is considered to be one of the most important natural selection that contributed to human evolutionary success. There are considerable application scenarios where a dexterous robotic hand could be invaluable such as disaster struck areas where the robot may have to interact with unfamiliar environment. It has been an elusive goal to enable a robot to master the human-level grasping skills. Recent studies on robotic grasping have focused on two or three-fingered robotic clamps [PelossofSVMGrasp, Spiers2Finger]. The research of grasping planning for an anthropomorphic robotic hand is more challenging and deserves more effort [BonilaSoftHands5Fing, Morales2006VisualPlanning5fingers].

Behind this simple task of grasping an object, the human brain is executing a series of sub-tasks with the associated decision-making and error-correction process in real time. The brain selects and executes appropriate motor strategy learned earlier by the human sensorimotor apparatus. These learned strategies, also called as action-phase controllers [NatureMechanoreceptors]

, utilize the input sensory signals and corresponding predictions by the nervous system to produce motor commands to accomplish the given motor task. The action-phase controllers accurately estimate the specific motor output required using the information about an object’s physical properties and the current configuration of the hand. The inspiration for this study is the human grasping mechanism - not only from the sensorimotor task of human grasping, but also the learning process itself. This study is therefore, an exploration in emulating a part of the human brain’s action-phase controller model to accomplish grasping using a humanoid robotic hand.

Figure 1: Framework of the proposed approach.

In this paper, we propose an approach to emulating human grasping strategies without complete sensing of object attributes, as shown in Fig. 1. By using labels and descriptions, object attributes are retrieved and augmented from a knowledge base that is scraped from online webpages. The extended object attributes include dimension, mass, shape, texture, fragility, material, and stiffness. We design a neural-network model to learn human grasping strategies for target objects with various physical attributes. The optimal grasping strategy is deployed to the anthropomorphic robot hand by a multi-constrained inverse kinematics of grasping topology and scales.

Human hands have 20 degrees-of-freedom (DoF) each (not including the wrist joint), thousands of mechanoreceptors [NatureMechanoreceptors] and therefore, a significant amount of the brain’s resources are dedicated to grasping tasks. Understanding human grasps is not a trivial task. Most of the efforts in understanding grasps [Cutkosky1989, Feix2016l] have been to breakdown the human grasping behavior into discrete classes. A structured classification of grasps is discussed in [Cutkosky1989] based on object shapes and task requirements. More recently a new and more comprehensive version of the Grasp taxonomy has been developed [Feix2016l, Heinemann2015humangrasptransfertorobothands] and refined by de-coupling them from the object shapes and the tasks being performed. A neuroscience-based study reports that hand posture can be decomposed into very few general configurations and that the finer adjustments can be achieved by superposition of such grasp poses [SantelloNeurosciencePCA]. Built on this concept, a method of using “eigengrasps” to reduce dimensionality of grasps was proposed [CiocarlieEigenGrasps]. Reducing dimensionality is a necessary step to make the problem of learning grasps tractable.

Robotic dexterity has long been a difficult goal. Earlier methods involved analytical approaches to calculate object affordances and contact forces to determine grasp successes [Miller2003ShapePremitives, JainGraspDetect2fing]. Knowledge based systems and expert systems have been employed to choose grasps [Cutkosky1989, Stransfield1991KBGrasping] where in the mechanism of grasping are broken down into discrete deterministic rules. But the sheer number of variations of human grasps and the difficulty in modeling various grasp scenarios limit such approaches to few narrow applications. Recent proposals have focused on learning methods [LiGraspUncertainity, Herzog2014Shape-templates, KROEMER2010GaussianBandits, AIVALIOTIS2017MLforCmpxPrts, MadryKragicPGM, Song10learningtask]

, especially application of deep learning methods to learn grasps

[DBLP:journals/corr/PintoG15, LenzDeepLearn, SaxenaAndNGNovelObjectsVision].

It is the redundant DoF of multi-fingered hands that enables dexterity of grasping and manipulation. There may be many possible strategies to grasp an object, and the optimal one depends on the affordances of the target object. Humans have the ability to apply proper grasping strategies for unfamiliar objects based on simple descriptions or formed association with known similar objects. As complete sensing of all object attributes is unworkable in industrial applications, robots would stand a better chance of success if they can imitate this human awareness and knowledge with incomplete sensing. We develop a knowledge base by mining object attributes online, and identify the best match from the dataset by using natural-language object descriptions as input.

The AR10 humanoid robotic hand in this work is is equipped with 10 servos and limited force feedback. Predicting 10 joint angles given a list of object attributes, is an ill-posed problem due to the infinite ways these joint angles can be configured. In this paper, a novel method is being proposed to make this problem tractable. The joint angle configuration space is discretized to consist of various human grasp types, whereby only the specific grasp type needs to be learned. The variations resulting out of differing object sizes, are addressed by introducing a scaling factor to the joint angles. This approach makes the problem of learning five-fingered grasps tractable by discretizing the grasp space and reducing the dimensionality of the problem. The problem of grasp selection could have been treated as a multi-class classification problem involving the selection of one of the possible grasps from the human grasp primitives. However, complexity exists in grasp labeling. There is no one ideal way to grasp a given object. Humans tend to choose grasps based on the object’s position, orientation, intended action and sometimes even making arbitrary grasp choices. So, the problem is not to choose an ideal grasp, but to choose one from a set of the feasible grasps, which perhaps would also be a preferred human grasp. To achieve this, multiple human grasping trials were conducted, and frequencies of the grasps were used as the labels against each object. A deep neural network model was trained on this labeled dataset to estimate probabilities of various grasp types conditioned on object’s physical features. The success of the approach is evaluated by validating the most probable (predicted) grasp against the feasible set of human labeled grasps.

Human grasping is a complex process with disproportionately large portion of the human sensorimotor apparatus dedicated to grasping. Therefore, it is no surprise that robotic grasping is a complex and as yet unsolved problem. The contribution of this paper is to further the knowledge and understanding of human grasping in the context of emulating human type grasps on a five-fingered robotic hand. In order to demonstrate this idea, a set of everyday objects are chosen to train the robot, to impart the knowledge and experience that is needed for it to succeed at grasping. Multiple learning models with novel concepts are developed and validated in the course of exploring the five-fingered robotic grasping problem. The models are tested through simulations and experiments with the physical robot. The major contributions of the paper include:

  1. The paper addresses the challenge of acquiring object attributes without complete sensing. A distance metric is proposed to query most similar objects in the developed knowledge base by using simple object descriptions.

  2. The paper designs a neural-network model to imitate human abilities in applying optimal grasping strategies for dexterous grasping. A well designed grasping strategies with grasping topology allows dexterous grasping of objects without precise attribute information.

Ii Object Affordance Acquisition from Knowledge Base

It is generally challenging to measure complete object attributes in industrial applications, including precise 3D dimension, materials, rigidity, and textures, but object categories can be recognized by machine learning algorithms. In addition, it is straightforward to describe important object attributes in natural language. We therefore developed a knowledge base of object attributes by mining online object information from retailer websites. By referring to the knowledge base, we can acquire extended object attributes by object labels or short descriptions for the selection of optimal grasping strategies. In this section, we define the dominant object attributes for dexterous grasping, design the parsing algorithm for key attributes from natural-language descriptions, and propose a novel distance metric for attribute acquisition from the knowledge base.

Ii-a Dominant Object Attributes in Grasping

Human grasp strategies depend on numerous factors including object shape, size, weight, texture, stiffness and sometimes fragility, temperature, wetness [NapierPrehenileMovements]. With our goal being able to understand these object features by parsing natural-language descriptions, we had to be parsimonious in our choice of object attributes. Based on findings coming out of previous studies, the following set of features, shown in Table I were prioritized for data collection. These physical attributes of the object significantly influence grasping decisions.

Feature Description Value Range
Dimensions along orthogonal directions s.t.
Mass
Shape classification [Cutkosky1989] thin, compact, prism, long, radial
Rigidity of the object rigid, squeezable, floppy
Texture medium, smooth, rough
Fragility sturdy, medium, fragile
Simplified material types fabric, glass, metal, paper, plastic, rubber, wood, other
Table I: Primary object attributes in grasping.
# Object Length (a) Width (b) Height (c) Mass Shape Texture Fragility Material Stiffness
1 calculator 15.4 7.9 1.5 116 thin medium medium plastic rigid
2 water bottle 21.5 7.2 7.2 660 prism smooth sturdy metal rigid
3 salt shaker 8.2 3.1 3.1 82 prism smooth sturdy metal rigid
4 computer mouse 10.6 5.9 2.5 79 prism medium medium plastic rigid
5 mini rubix cube 3.0 3.0 3.0 12 compact smooth sturdy plastic rigid
6 wood wedge 6.0 3.0 1.5 11 prism rough sturdy wood rigid
7 wood disk 7.2 7.2 2.0 60 compact rough sturdy wood rigid
8 tennis ball 6.4 6.4 6.4 56 radial rough medium fabric soft
9 stapler 13.2 6.8 3.6 151 prism smooth sturdy plastic rigid
10 kitchen scale 20 20 1.8 830 thin smooth sturdy glass rigid
Table II: Object knowledge base (cf. Table I for descriptions of the features).

Enormous object descriptions are available in the internet particularly in retailer webpages such as Amazon and Walmart. The object descriptions typically include object dimensions, weight, and materials. We developed a web scraper to collect object information (the source code is available online at https://github.com/hhelium). The web scraper downloads product description webpages and uses pattern search to discover object attributes. The knowledge base consisted of dimension measurements, mass, rigidity, material, texture, fragility, and shape classifications. The missing attributes for some objects are manually labeled and annotated. Examples of the objects and attributes in the knowledge base are shown in Table. II. Given object labels or short descriptions will be mapped to the feature set corresponding to the described object or a similar object

(1)

where the meanings of the attributes are defined in Table I. We will discuss the description parsing and mapping in the following sections.

Ii-B Parsing Object Descriptions

The problem to address in parsing object descriptions is to estimate significant object features that determine object categories. At the same time, the method is required to be resilient to missing, partial or incorrect descriptions. Object descriptions may specify object details such as its approximate dimensions, e.g., “it is about ten centimeters long”, or materials, e.g., “it is made of plastic”. Though not accurate or specific, the object descriptions are informative when the object’s descriptive and quantitative information is contained. To specifically address free-form descriptions where object object features are described in any format or order, we designed a natural-language parser as shown in Algorithm 1. The natural-language statements are cleaned up by lemmatizing and removal of stop words. Each word in the statements is then tagged with parts of speech (POS) labels based on standardized tags from the Penn Treebank project [PennTreebank]. Examples of POS Tag Nomenclature include JJ-Adjective, IN-preposition or conjunction, CD-Cardinal digit, CC-coordinating conjunction, NN-noun, and RB-adverb. Of special interest to our study, are any available quantitative and qualitative descriptors of the object(s). We look for expressions such as “two centimeters long”, “made of plastic” or “very rough” using regular expressions as shown in the algorithm.

Input: Object description string: ObjDescrption;

Input: Regular Expression: regex =

{<JJ.?>*<IN>*<CD.?><CD.?>*

<CC.?>*<CD.?>*<NN.?>*<RB.?>

*<JJ.?>*<IN>*<NN.?>*<JJ.?>*<NN.?>?}

Output: Array of dimensions of the object:

WordTokstokenize(ObjDescrption)

POSToksApplyPartsofSpeechTokens(WordToks)

PhraseTreePerformChunking(POSToks, regex)

for each Chunk in PhraseTree do

if QuantitativeDescriptor in Chunk then

ParseToNumber(Chunk)

end if

if QualitativeDescriptor in Chunk then

ParseToCategorical(Chunk)

end if

if null in l then

DataImputation(l)

end if

end for

Algorithm 1 Object description parsing.

The extracted phrases (or chunks) lead us to individual feature descriptors. When there are more than one dimensional descriptor, the largest value is assigned to feature , the smallest to feature and the intermediate one to feature

. One of the challenges is that if we do not have all feature descriptors then we have null values. One example is when the object has a radial symmetry, it is described only by diameter. For such cases, we perform data imputation using a rule-based approach of estimating the missing dimension based on the other available dimensions of the object. The rule itself was derived from the priors in the data. The success of this model is evaluated by scoring the parsed values with the measured or labeled values and the scores are used to improve the algorithm.

Ii-C Object Knowledge Acquisition

In addition to the basic object attributes in the description, we desire to acquire more features from the knowledge base. Even with a reasonably detailed elucidation of an object’s features, object descriptions tend to be either imprecise or incomplete. For example, the general tendency is to round-off dimensions and mass, often missing to mention certain features such as material type or texture. A human can still work with the information available only because of the recall of having seen or held such an object. The curated knowledge base of objects and their physical features emulates human memory.

Let represent features corresponding to an object from this dataset where and . The features parsed from descriptions of a reference object is represented by . It should be noted that could possibly have empty values for some of the elements due to incomplete object descriptions. The problem is to find the most similar object to the reference object in the knowledge base . By identifying an object closely matching the description of the reference object, we continue to retain the ability of choosing the most suitable grasp because the reference object, most likely, has physical features very similar to the object chosen by the algorithm.

Several distance metrics, such as Euclidian, Minkowski and Cosine distances, have been developed to measure the proximity of vectors in n-dimensional space

[ChaDistance, GomaaTextSimilarity]. To increase the identification accuracy and the confidence of object match, it is necessary to not only ensure the proximity of the points in the normed vector space, but also to ensure proximity of each individual feature. To that end, we calculate the probability of the object being mapped to the reference object given the distance of the feature. The overall probability of mapping object to the reference object is the joint probability over all the features of the object. This approach ensures that only that object which matches the reference object’s every individual available features is the one that results in a highest probability value. The proposed distance metric, named Joint Probability Distance, is defined as

(2)

One of the advantages of using this distance metric is that the probability of the match decays at a much faster rate as each of the features deviate from the reference. This helps in non-linearly increasing the distance of unlikely candidates and filtering out the unlikely matches with more confidence.

This distance metric can be used on a dataset with contains a combination of continuous and categorical (transformed to one-hot binary encoding) features without any need for data normalization. For example, material of an object is a categorical feature with eight possible text values. The Material property feature can be easily converted to eight features with one-hot encoding. This distance metric will work well with such categorical variables. The accuracy of distance metrics including Euclidean, Minkowski, Cosine, K-D tree, and Joint Probability was compared and results are shown in Fig. 

2. The proposed Joint Probability metric achieved the best recall accuracy for the knowledge base.

Figure 2: Comparison of object recall accuracy of various distance metrics.

Iii Human-Like Dexterous Grasping

The grasp of a multi-finger robotic hand defines a set of angles of the finger joints, and the magnitude of the contact forces applied by the fingers and palm to an object at the contact points. The objective here is to emulate human dexterous grasping by mapping object features referred in the knowledge base onto a grasp prioritization. The referred object features may be inaccurate or even erroneous due to rough measurement and description. We therefore implement grasp strategies in terms of grasp topology and scales, which enable imprecise measure of object dimensions and location, thus improving system robustness and adaptivity. In this section, we define dexterous grasp taxonomy and implement grasping strategies on a robotic hand by extending our prior work [BRaoHHe-Self].

Iii-a Grasp Definition

Figure 3: AR10 Robotic Hand with 10 degrees of freedom (DoF).

Grasps can be defined by the finger joint angles for a humanoid robotic hand with multiple fingers. As shown in Fig. 3, the AR10 robotic hand used in this work has ten DoF and limited force feedback. A grasp can therefore be defined in the 10-dimensional joint configuration space as

(3)

where each joint angle can continuously vary over the operating range of the servos to result in infinite grasp patterns. Owing to the redundant DoF, there may be multiple feasible grasp strategies associated with one object, so it is intractable to design a deterministic grasping model for various objects. We discretize the configuration space and map configuration space into a space with reduced dimensionality by

(4)

where represents human grasp topology and is the scales that determine the completion of the grasp. Each grasp topology is a unique combination of joint angles representing one of the human grasps with chosen such that mimics a particular human grasp type from the grasp taxonomy. The grasp topology spans the entire configuration space, and a grasp can be represent by

(5)

whereby a range of grasps can be defined by human grasp topology and the time-variant completion scale . We will learn a mapping between object features and grasp topology, and implement the completion scale by inverse kinematics.

Iii-B Dexterous Grasp Taxonomy

Robotic grasps can be made effective by emulating human grasps, and we desire to choose a suitable taxonomy of human grasps. Although comprehensive grasp taxonomy is available, we decided to adopt the Grasp Taxonomy presented by Cutkosky [Cutkosky1989] for this work. Even within this taxonomy, we have restricted it to the six higher level classifications, because the finer adjustments can be obtained by combining these six grasp types with the scalar. The chosen human grasp classification and the nomenclature for each grasp is shown in Fig. 4, where the prefixes and stand for Power and Precision grasps [Cutkosky1989, NapierPrehenileMovements].

Figure 4: Human Grasp Taxonomy derived from [Cutkosky1989].

The grasp classification is one of the grasp types drawn from the set of human grasp primitives

(6)

Grasp scales are determined by the dimensions , , around which the grasp closure occurs, as illustrated in Fig. 5. This labeling convention is commonly adopted in the literature [Feix2016l], facilitating the computation of hand closure in forward and inverse kinematics. Grasp dimension is defined as

(7)

The grasp scale is therefore a function of the grasp type and grasp dimensions

(8)

For most object-grasp associations, we found the selection of grasp type defines the selection of grasp dimensions and sizes; but for certain object-grasp associations, the choices of grasp dimensions were inconsistent for different attempts. Such confusion was mostly observed for objects when or . To address this confusion problem, a new grasp classification was created by concatenating the grasp type and dimension. For example, other than ‘circular’ grasps, no other grasp type uses ‘’ dimension; ‘thin’ grasp cannot be executed along the longest dimension ’’. The extended grasp taxonomy is defined as

(9)
Figure 5: Illustrations of grasp topology and size.

Iii-C Learning Grasping Strategies from Human Knowledge

Most studies [NapierPrehenileMovements, Cutkosky1989, NatureMechanoreceptors] attempting to understand and codify human grasps have concluded that human grasp choice is a function of object affordances (geometry, texture etc.) and the task requirements (forces, mobility, etc.). Attempts to assign one most suitable grasp for a given object-task combination have not been conclusive. The major problem is that even for one specific object-task combination, there are multiple grasp choices possible, which ofter appear to be arbitrary and not amenable for deterministic modeling. Human grasp choices nevertheless do tend to cluster when studied over a large set of objects. Both the clustering effect and the confusion between grasp types can be seen in the data presented by [Feix2016l], which shows that a single object could be held in multiple different grasp types in the course of picking or handling. There is no one-to-one mapping of one object to one grasp type.

The problem of grasp selection is therefore not selecting one ideal grasp type but one of the many feasible grasp types in human grasp taxonomy for the given context. To that end, we plan to learn the mapping from features into grasp topology distributions

(10)

We designed a neural network to model the probability distribution over all grasp classes , as illustrated in Fig. 6

. The network is designed with cross-entropy loss and optimized using stochastic gradient decent algorithms. The loss function is defined by cross entropy that measures the deviation between the ground truth and predicted probability distribution

(11)

where with as the number of observations and is the index of grasp topology.

Figure 6: Grasp Selection Neural Network Classification Model.

The grasp with the maximum probability is chosen by

(12)

where is the acquired object features. The predicted grasp configuration contains information regarding the grasp type and object dimension along which the grasp can be executed, so can be easily decomposed into grasp type and grasp dimension , which can be used subsequently to calculate robot hand configuration. The optimal grasp type is chosen as the one corresponding to the highest probability from the predicted probability distribution.

Because the model predicts probability distributions, we defined two scoring metrics for training and evaluation of the model. The predicted grasp choice is scored as a success if the same grasp type was chosen at least once in the human-knowledge database. The feasibility of the grasp is scored as

(13)

where

(14)

is the grasp topology with the maximal probability, and is defined in (9). The feasibility score is representative of the ability of the algorithm to pick a feasible grasp for a given object. The match score metric is defined as

(15)

This match score is representative of the ability of algorithm to predict the most frequently applied human grasp as the grasp with the highest probability for a given object. In other words, is akin to the accuracy, had this grasp learning problem been treated as a multi-class single label classification problem. This metric is much more stringent and therefore we can expect the match score to be always lower than feasibility score

(16)

We used the feasibility score as the primary scoring metric, for the objective is to find one feasible grasp that can be successfully executed by a robot.

Iii-D Deploying Grasp Strategies

Grasp types are determined by grasp topology, and the grasp size for a particular grasp type corresponds to object dimensions. The grasp size is essentially the distance between the virtual fingers of a particular grasp type [Cutkosky1989]. The grasp size can be computed or estimated from object dimensions based on geometric relations, as illustrated in Fig. 5. The grasp types and sizes are implemented by inverse kinematics of the hand and fingers.

We developed a multi-constrained inverse kinematics of the robotic hand to deploy grasp topology and finger closure [yamane2003natural]. The multi-constrained inverse kinematics enables multi-point planning of each finger in the process of hand closure and grasping. We considered two levels of kinematic constraints in grasp strategy implementation: high-priority and low-priority constraints. The distance between virtual fingers (finger tip closure) meets high-priority constraints on distal phalanges, and the trajectories of the middle and proximal phalanges satisfy low-priority constraints. The inverse kinematics transforms the trajectory of points on fingers to angular joint velocities.

The trajectory of finger tips (distal phalanges) is planned for , which corresponds to the object dimensions. Not all objects have regular geometric shapes and the calculated may deviate from the actual size on such objects, as shown in Fig. 7. We adopted a straight-line path

(17)

where is the time scaling for the ith finger, and is the starting distance between the virtual fingers. The closure of the fingers are controlled following . The AR10 Robotic hand has 10 DOF with 9 actuators controlling the fingers and one controlling the opposing thumb action. The robotic hand has limited and inaccurate force measurement by the Force Sensitive Resistors (FSR) that are attached to each finger. The closing of the fingers stops until sufficient contact forces are measured.

Figure 7: Illustration of variations in virtual finger distance for the same grasp type: prismatic grasp (rp).

In the experiments shown in Fig. 8, we found the distance between virtual fingers can be linearly approximated by object dimensions within a limited range of motion

(18)

We learn the parameters and by generating data on the physical robotic arm and fitting a linear model between and . For each grasp type, we varied the joint angles, measured the distance between virtual fingers

, and fit a linear regression model to learn the parameters by minimizing least square errors.

Figure 8: The relation between virtual finger distances and object dimensions.

Iv Experiments

We conducted the experiments to evaluate the performance of object affordance acquisition, grasp strategy selection, and robot grasping. Leveraging existing grasping database [bullock2015yale], we collected more data for experiment and training, including object attributes, short natural-language descriptions, and human grasp strategies. The developed methods include natural-language parsing algorithms, models for grasping strategy learning, object identification, and robot grasping. We validate the methods and quantify the performance on collected data by executing the grasps physically on a robot with unfamiliar objects.

Iv-a Experiment Setup

The anthropomorphic robotic hand integrates an AR10 robotic hand, a Rethink Sawyer robot (with an in-arm camera), and an Intel RealSense RGB-D camera installed on the wrist, as illustrated in Fig. 9. The robotic hand has limited force sensing through the Force Sensing Resistors (FSR) attached to finger tips, so we focused on hand configurations and planning and used the force sensors to examine contact conditions. The goal of the experiment was to grasp an object, which was described by a label or short incomplete description, in a proper manner to the object affordance. We seamlessly integrated the models and modules in ROS Python libraries that control the AR10 and Sawyer robots. The experiment is set up as follows:

  1. To emulate industrial scenarios with incomplete sensing, we only used the camera to locate and identify an object. The objects or tools for experiment are placed on the table with a fixed initial pose. We utilized the localization and recognition algorithms developed in our prior work [fujian19robotic, fujian20common, yan2018semantics]. The coordinates of the object on the table is back projected to the robot frame.

  2. We retrieved object affordances based on optional object descriptions, e.g., “A scientific calculator with plastic body. It is about fifteen and half centimeters long, 8 centimeters wide and appears to be more than one and half centimeters thick.” The recognized object labels are adopted when an object description is unavailable.

Figure 9: Robot Setup: Sawyer robotic arm with an AR10 robotic hand and in-hand sensors.

Iv-B Data Collection

We followed the design of the human grasping database [bullock2015yale] and extended the database by collecting grasping samples. Given the ergonomics of human grasps, objects were selected to capture sufficient variations in objects features and grasping strategies (samples are shown in Fig. 10). Short descriptions of the objects are provided along with object labels, covering shape, dimension, mass, rigidity, and texture if available.

Figure 10: Sample objects for experiments.

Grasping strategies depend on object affordances and tasks. In this paper, the task definition is restricted to securely holding and supporting an object in midair. The intent (task) of the grasp and the object’s position and orientation may influence the grasp strategies; however, we kept these variables constant but focused on object features during the experiment. There could be multiple ways of grasping for most objects, so the experiment was designed to replicate potential grasping multiple times. At each attempt, the subjects were encouraged to try alternate ways of grasping the object while ensuring the comfort and security of the grasp. At the end of this experiments, we had a frequency distribution of human preferred grasp types. Sample data is shown in Table III. It is inevitable that the optimal grasp labels are subjective to some extent due to personal preferences and background. There are indeed multiple feasible or optimal strategies for one scenario.

# Object

rc.ab

rc.bc

rp.b

rp.c

wc.abc

wh.bc

wh.c

wp.bc

wt.c

1 calculator 0 0 5 2 0 0 0 0 2
2 water bottle 0 1 1 2 0 5 0 0 0
3 wood cylinder 0 2 5 2 0 0 0 0 0
4 cardboard box 0 0 5 2 0 0 0 0 2
5 mini rubik’s cube 1 4 3 1 0 0 0 0 0
6 wood wedge 0 2 4 2 0 0 0 0 1
7 wood disk 4 0 2 2 0 0 0 0 1
8 tennis ball 1 1 2 1 4 0 0 0 0
9 wood piece 2 4 2 1 0 0 0 0 0
10 plastic cap 5 0 2 1 0 0 0 0 1
Table III: Grasp (Frequency) Labels – Sample Of 10 Labels.

Iv-C Object Affordance Acquisition

The effectiveness of the natural-language parses was scored and validated by the Ordinary Least Squares Regression model. The final model was able to fit with an

of overall for all dimensions and for mass estimations. The regression fit for dimension estimations is shown in Fig. 11. The primary source of errors in this step are the approximated dimensions in descriptions. This results in larger percentage deviations when describing smaller dimensions. Categorical labels for material, shape and rigidity were also scored, where the scoring matrix for material classification is shown in Fig. 11

. The objects were classified under “other” when description details are insufficient. The main source of errors in material recognition is omitted features from the description. This behavior stems from preconceived assumptions that such features are very obvious and do not require specific mention. For example, when the object is made of plastic, the subjects tend to omit any mentions of the object’s stiffness, assuming plastic objects are rigid.

Figure 11: Left: Parsed vs measured dimensions. Right: ‘Material’ type prediction score.

Object affordances, including shapes, sizes, weights, and textures, lead to specific grasping strategy [NapierPrehenileMovements]

. We desired to prioritize the influence of the features on grasping strategies. Such a prioritization would help the learning of grasping strategies and the design of perception algorithms. To that end, we used a recursive feature elimination (RFE) method along with a Random Forest classifier to rank the features

[scikit-learn]. Starting from the most significant factor, the ranking is dimension, shape, mass, texture, material, stiffness, and fragility. The result is generally in line with prior studies. It was interesting to note that the most important dimension was the intermediate dimension followed by the shortest dimension and then by the longest dimension . For most objects, especially larger ones, humans tend to grasp it along the shorter dimension primarily because of comfort of holding.

Figure 12: Plot of assigned object indices vs indices of objects chosen by the Joint Probability Distance Metric. Points offset from line show mis-identified objects. E.g. Object number 87 (a thin laptop) is mis-identified as object 98 (a photo frame)
Figure 13: Sample grasping strategy determination: ground-truth vs. predicted grasping.

Object features parsed from object descriptions are used to acquire full features of similar objects from the collected object database. We performed contextual search based on the proposed distance metric, and validate the performance of the methodology. The parsed object features are commonly incomplete and inaccurate because of approximation errors, missing information, and parsing errors. We used the subset features to query and match the object being described to a similar object in the database. The test results of acquisition of 100 random objects are plotted in Fig. 12

. As the confusion matrix show, the overall accuracy was 92%. The errors primarily stem from incomplete or insufficient information, similarity to multiple objects, and wrong descriptions. The approach demonstrated robustness in object acquisition under uncertainty, when partial object features were incorrect. Despite the confusion, it was interesting to note that the objects chosen by the algorithm were physically identical to the objects being described. A mis-match is therefore not necessarily detrimental to the grasp strategy. It is still possible to identify the correct grasp with the wrong object as long as the object is physically similar to the target.

Iv-D Robotic Grasping

We developed the neural-network model to learn grasping strategies as proposed in Sect. III-C, and optimized the model in terms of cross-entropy. The input of the model is the acquired object features, and the output is the grasping strategies corresponding to human preference and knowledge. The grasping strategies were represented by normalized probability distributions. The results of grasping strategy determination with scores are reported and compared to the ground truth in Fig. 13. The feasibility score of the model is 100%, which was defined as the hit rate of the predicted grasp strategy in all human preferred grasps. The experiment shows that the model’s capability in picking feasible (human validated) grasping. The match-score , on the other hand, measures the accuracy of the prediction considering only the most preferred human grasping. The experiment demonstrated that the max-match rate was around 80% for the test objects.

To further examine grasping performance, we performed grasping experiments on the robotic hand platform. Test objects are placed on the table with a default initial orientation, and the objects are located and identified by the developed object recognition algorithms. A short description of each object was provided to cover its estimated dimension and materials. The robot autonomously chose the grasp strategies according to the acquired object affordance from the object database. The success of a grasp is validated by the security of grasping after brief maneuvers including lifting, holding and placing. A grasp is deemed as a success if the object does not fall during the maneuvers. The overall success rate of grasping was around 89%, and some experiment results are shown in Fig. 14. One failure case was the grasping of the capacitor, where the model predicted the most preferred human grasping strategy but failed to securely grasp the capacitor; the failure could be attributed to the limitation of hand dexterity or inadequate friction. Another failure case was the grasping of the plastic container, where the model predicted a different grasping strategy (rp) instead of the most preferred human grasp (wt). Though the predict strategy was one of the grasps used by humans and hence valid, the robot could not manage to secure the object. The other failure case was the grasping of the plier, where the model predicted the most preferred human grasping strategy (rp), but the plier changed formation during the grasping and fell. The developed system does not possess the capability in adjusting strategies during the process of grasping. There were many successful cases of grasping, where the predicted grasping strategies were different from the most preferred ones.

Figure 14: Robot grasp trials with 10 test objects. stands for human labeled grasps; stands for Learned grasp. in red color indicates mismatch with human grasp preference. ’Failure’ indicates robot’s inability to grasp the object. ‘Success’ indicates that the grasp was successfully executed on the AR10 Robotic Hand. More percentage of failure cases are demonstrated for better understanding of the limitation of the method.

Grasping involves a series of decision making processes using experience and knowledge of the physical world as the basis. Human grasp strategies vary even when most of contextual variables are fixed. The existing human grasping data validated this interpretation: most objects are associated with multiple grasping strategies for the same tasks. Furthermore, it is still impractical to grasp one object without measuring or acquiring contextual information. Familiarity with a specific object is important knowledge that robots need to acquire before attempting a grasp with the assistance of object recognition algorithms. In an event of confusion, it still identifies the closest object with similar physical features, ensuring that there is enough information to continue onto grasp execution.

We designed the sequential machine-learning model to emulate human decision-making processes. In such a sequential model, errors from one model could permeate or propagate into the next model. In the experiment, while there were errors in each stage of and prediction, we did not observe a significant impact on the final grasp execution on the robot. The primary reason could be that, while human grasping is complex, it is also highly resilient to external perturbations of contextual variables. When modeling robot gasping using human grasp primitives, this resilience behavior was emulated as well. For example, there are multiple ways to grasp an object, so it is less likely to choose a wrong grasp as we have seen from the results of our deep learning model (100% match score). The other reason is that the experiment objects were designed with the intent of handling by five fingered hands, so when there is a mis-calculation, e.g., grasp dimensions, the fingers conform to the object shape and still result in a secure grasp.

V Conclusion

This paper has demonstrated the approach to applying a proper strategy to grasp an object without complete sensing of object affordance. The framework of grasping strategy determination, object affordance acquisition, and robotic grasping deployment was developed through a combination of probabilistic and machine learning models in order to teach robots to grasp unfamiliar objects. The strategy determination can predict human grasping knowledge with a 100% feasibility score and a 80% match score. The designed distance metric outperformed other popular distance metrics and achieved overall 92% accuracy in object feature acquisition. These learning models could be extended to include additional inputs for position, orientation, and grasp intent for a broader applicability. With more sophisticated robotic arms built with multiple tactile sensors and precision force control, we can train models with more data and realize better performance in executing the grasps on real world objects. In summary, the experiments show that emulating human behavior is a practical way to build an autonomous robotic hand capable of adapting to unfamiliar environment.

References