IROS2019国际学术会议论文集 1604

Human Intention Inference and On Line Human Hand Motion Prediction for Human Robot Collaboration Ren C Luo and Licong Mai Abstract With recent development of robotic technology it is increasingly common that robot coexist with human in which humans and robots share a common workspace and work in close proximity To maintain effi ciency and ensure safety under these circumstances robot should have the ability to predict the future human motion based on the observed on going motion In this paper we present a methodology for on line inference of human intention and prediction of human hand motion The proposed framework is built using Probabilistic Dynamic Movement Primitive PDMP In the off line stage a set of PDMPs is constructed based on the recorded demonstrations and they will then be used for inferring human intention and predicting human hand motion in the on line stage A proof of concept evaluation is carried out in a tabletop manipula tion task Experimental result shows the proposed framework achieve high performance in human intention inference and in the trajectory similarity between the predicted and the actual hand movement under the normally defi ned environment We also show the proposed framework can adapt and generalize to the newly defi ned environment I INTRODUCTION The industrial manufacturing process had been dramat ically revolutionized by the deployment of industrial robot over the last 60 years Benefi t from the reliability and high ef fi ciency of robot manufacturers are able to produce products with shorter cycle time and lower cost To ensure safety most of the existing industrial robots are caged and separated from human Although such mechanism is effi cient it prevents robot from interacting with human and hence excludes robot from many potential applications With recent development of robotic technology it is increasingly common that robot coexist with human in which humans and robots share a common workspace and work collaboratively in a close proximity Under this circumstance how to maintain the high effi ciency of robot while preventing human from injuries caused by robot become extremely important Our work addresses this issue A common approach to ensure safety in Human Robot coexisting environment is to consider the human as a dynamic obstacle and the robot constantly monitoring the human movement and iteratively re planning and executing its motion accordingly to avoid confl ict with human Although this method had proved to be feasible 1 3 it only considers either the current human movement or the human movement in the immediate future which may lead to an unnatural and ineffi cient robot Ren C Luo is with Department of Electrical Engineering National Taiwan University No 1 Sec 4 Roosevelt Road Taipei Taiwan 106 renluo ntu edu tw Licong Mai is with Department of Electrical Engineering National Taiwan University No 1 Sec 4 Roosevelt Road Taipei Taiwan 106 lcmai ira ee ntu edu tw Fig 1 Overview of the proposed framework behaviour Inspired by Human Human Collaboration HHC one possible solution for maintaining effi ciency and safety during Human Robot Collaboration HRC is to integrate the long term prediction of human movement into the mo tion planning framework of the robot for which the robot observes the on going movement of human and generate an accurate long term prediction of human movement By taking the prediction result into consideration robot can plan its motion accordingly and result in a more effi cient HRC The main goal of our research is to establish a motion planning framework for autonomous robot operation which will take into account not only the current but also the future human motion As many others 4 7 11 12 our work start with the assumption that the human motions are goal directed Since the hand position is one of the most infor mative features in human manipulation movement this paper will focus on intention inference and hand motion prediction based on the observed on going hand motion Note that although we only consider the prediction of hand motion in this paper however it is easy to generalize the proposed framework to predict future arm motion In a fully developed stage this research will be able to provide robot the ability to anticipate future motion of its human partner and plan its motion accordingly The primary contribution of our work is a framework for on line inference of human intention and prediction of future hand motion The proposed framework is built using Probabilistic Dynamic Movement Primitives PDMPs and an overview is given in Fig 1 Our work involves two stages in the off line stage a set of demonstrations is collected and is used to train the PDMPs While in the on line stage the PDMPs are fi rst used to infer human intention and the inference result will then be used for hand motion prediction 2019 IEEE RSJ International Conference on Intelligent Robots and Systems IROS Macau China November 4 8 2019 978 1 7281 4003 2 19 31 00 2019 IEEE5958 The rest of the paper is organized as follow after giving a brief literature review in the next section we introduce the mathematical and algorithmic details of our method in section III In section IV we present the experimental results and further discussions Finally the conclusions will be included in section V II PREVIOUSRESEARCHWORKS Our work contributes to the fi eld of human intention inference and human motion prediction which is a crucial step toward fl uent Human Robot Collaboration HRC Prior works in this fi eld can be divided into two categories the deterministic method and the probabilistic method For the deterministic method one of the most common methods is minimum jerk model which assumed the speed profi le of human hand movement fi ts the minimal jerk cri terion Under such assumption algorithms for hand motion prediction had been proposed and integrated into the motion planning framework of the robot in 8 10 Different from 8 10 that simply assumed the underlying cost function of human hand movement is its jerk 11 12 used an Inverse Reinforcement Learning IRL algorithm to recover the cost function from a set of demonstrations and the recovered cost function will then be inserted into a trajectory optimizer to predict future human motion With recent development of deep learning Deep Neural Network DNN had also became a popular choice for modelling human movement In 15 17 25 27 various DNNs with different structures were proposed to model and predict human motion In contrast to probabilistic method the aforementioned approaches can only provide a deterministic result of motion prediction that is without confi dence information about the provided result As opposite to the deterministic method probabilistic method tries to capture the variability of human motion by establishing a probabilistic model over the demonstrations Various probabilistic models such as Hidden Markov Model HMM 13 Gaussian Mixture Model GMM 4 6 7 Probabilistic Flow Tubes PFT 5 and Gaussian Process GP 18 had been successfully utilized to encode the demonstrations and hence predict human motion However these models are constructed directly over the demonstrations and thus diffi cult to generalize to the new environment which is a major challenge in human motion prediction In 28 32 33 different task parametrized probabilistic models had been proposed to encode the human demonstrations which allow the robot to learn skills from the demonstrations and generalize them to the new situation Although the works proposed in 28 32 33 did successfully applied to robot however the way they adapt to the new environment may not be the same as how human react to the new environment Different from above probabilistic methods our work establishes the probabilistic model by encoding the demonstrations into the Probabilistic Dynamic Movement Primitive PDMP which is the probabilistic version of Dynamic Movement Primitive DMP There are two major reasons why we would like to encode the demonstrations into the PDMP Firstly it is well demonstrated in many researches that the DMP can readily adapt to the new environment by modifying its weights and or meta parameters initial goal state temporal scaling factor etc Secondly it is shown that the DMP can be modifi ed to preserve the shape of the learned trajectory while adapting to the new setting for instance in 34 the modifi ed DMP generates a human like motion trajectory while adapting to a time varying attractor The idea of applying Movement Primitive MP to HRC had also been proposed by several other researches In 29 the authors used a DMP based algorithm to predict human motion and retrieve the reactive strategy for robot motion planning based on the movement of human partner While the feasibility of the proposed method had been showed the representation of collaboration in the space of acceler ations increases its sensitivity to noise in the observations as it requires computing the accelerations in the weight retrieval stage 31 Similar algorithms also introduced in 30 31 where instead of DMP the authors used Probabilistic Movement Primitive ProMP to model the collaborative be haviour Moreover research 14 had also proposed a ProMP based algorithm to infer human intention and predict future motion during Human Robot Interaction HRI Although the ProMP based methods had showed lower noise sensitivity than the work in 29 as they retrieve the weights directly from the observations the lack of meta parameter makes them less fl exible in the unstructured environment Different from 14 29 31 our work jointly estimate the states and the weights via PDMP which avoids computing the accel erations in the weight retrieval stage and provides better fl exibility in the unstructured environment Furthermore the joint estimation allows us to capture the possibly strong correlation between the states and the weights The details of the framework is provided in the next section III METHODOLOGY A DMPs and PDMPs Dynamic Movement Primitive DMP encodes a desired movement trajectory in terms of non linear differential equa tions 19 20 The original DMP can be viewed as a com bination of a second order Linear Dynamical System LDS and a forcing term While the dynamical system ensures the trajectory converge to the attractor goal the forcing term enforces the trajectory as similar to the desired trajectory as possible For a 1 D trajectory the equations are x x x gx x x fx s 2 1 s ss 2 where x x and x are the position velocity and acceleration gxis the goal of the trajectory is the temporal scaling factor and is set to 1 in this work The x xand sare the positive constant coeffi cients and xand xare chosen as x 4 xso that the eq 1 is critically damped The Eq 2 is the canonical system with s0 1 and it decays toward zero as time evolves The forcing term fx s is defi ned as fx s N i 1 xi s xi N i 1 xi s s gx x0 3 5959 where x0is the initial position and x i exp s cx i 2 2hxi 4 is the ithradial basis function kernel and hx i and cx i are the corresponding width and center Note that the superscript x indicates the DMP is trained for the movement in x dimen sion and it can be changed to y and z in the latter section By properly choosing the weights x i the DMP can represent arbitrary non linear function The weights x i can be learned from the desired trajectory via Locally Weighted Regression LWR or Locally Weighted Projection Regression LWPR More details about the DMP can be found in 19 20 Following 21 the DMP equations can be discretized by applying Euler discretization with time step t pk Apk 1 Buk 1 5 where pk xk xk and uk x xgx fx sk and A 1 t x x t1 x t B 0 t are the transition matrix and control input matrix of eq 5 DMP can also be transformed into a probabilistic version which is denoted as Probabilistic Dynamic Movement Prim itive PDMP Two types of PDMP had been proposed in the prior works authors of 21 construct the PDMP by adding a white noise term to eq 5 to model the transition uncertainty whereas authors of 22 construct the PDMP via Bayesian regression to capture the transition uncertainty with respect to the non linear forcing term Different from above works we construct the PDMP by utilizing the affi ne property of normal distribution This is achieved by modelling the DMP weights as a multivariate normal distribution x N x x and therefore the forcing term is also a normal distribution f sk N x sk x x sk x x sk T where x sk h x 1 sk sk gx x0 N i 1 xi sk x N sk sk gx x0 N i 1 xi sk i Then the equation of DMP will be transformed into pk Apk 1 Buk 1 k 6 where uk x xg x sk xand kis the transition un certainty with k N 0 B x sk 1 x B x sk 1 T that captures the variability of the demonstrations B Prior Weights Distribution One of the greatest challenges of this research comes from the fact that even though in the same task the duration of human motion will still vary Therefore before further processing the collected motion trajectories must be aligned fi rst In our work we fi rst compute the average trajectory for the task via Dynamic Time Warping Barycenter Averaging DBA The resulted average trajectory will then serve as a reference trajectory to align all the trajectories in the same task via Dynamic Time Warping DTW DTW is an algorithm that used to compute the similar ity between two time series with different lengths Given two time series with different lengths DTW searches for the optimal alignment path by minimizing the cumulative distance between two time series with boundary constraint monotonicity constraint and step size constraint 23 Based on DTW 24 had proposed DBA to average multiple time series with different lengths Given a set of time series x1 xN the general procedure of DBA is 1 Select a time series from x1 xNas the initial average time series x0 avg Note that the length of the average time series is equal to the length of the selected time series 2 Iteratively repeat the following two steps n times i Align every time series with the average time series xiavgand compute the corresponding warping path via DTW ii Update every points in xi 1 avg k by averaging all the points associate to xiavg k in i until i n Since the average duration is the most representative feature of the duration of the task the average trajectory will be initialized by the trajectory whose duration is closest to the average duration of the collected trajectories within the same task The DBA algorithm and DTW algorithm will then be used to compute the average trajectory and align the N trajectories within the same task After the alignment the aligned trajectories and average trajectory will be used to trained the corresponding DMPs and their weights 1 N avg will be used to estimated the prior distribution of the DMP weights with respect to the corresponding task We model the prior distribution of the DMP weights as a multivariate normal distribution N The mean is set equal to the weights of the average trajectory avg and the covariance matrix is computed as 1 N 1 N i 1 i avg i avg T C Reformulation and Parameter Estimation Since the recorded hand motion trajectories are 3 D we need to reformulate eq 5 as paug k Aaugpaug k 1 B aug g Bx k 1 x B y k 1 y B z k 1 z 7 where paug k xk yk zk xk yk zk Tis the augmented state vector diag x x y y z z is the fi rst coeffi cient matrix and g gx gy gz Tis the 3 D attractor which is the goal position The x yand zare the DMP weights for the movement in x y z dimension respectively and we further assume the weights of different dimensions are independent The transition matrix and control input matrices are Aaug I3 3 t I3 3 t I3 3 t Baug 0 t I3 3 Bx k 0 t nx x sk where diag x y z is the second coeffi cient matrix and denotes the Kronecker product We also defi ne nx 1 0 0 T ny 0 1 0 Tand nz 0 0 1 T The other two 5960 control input matrices By k and Bz k are similar to Bx k and are obtained by replacing nxby nyand nz and replacing x sk by y sk and z sk respectively Taking the observation model and the uncertainty of the weights into consideration the new PDMP can be obtained as paug k Aaugpaug k 1 u aug k 1 aug k 8 qk Caugpaug k 9 where qkis the Cartesian position and Caug I3 3 03 3 and N 0 q are the observation matrix and the obser vation noise respectively The control input and the transition uncertainty are uaug k Baug g Bx k x By k y Bz k z and aug k N 0 paug k with paug k Bx k 1 x Bxk 1 T By k 1 y B y k 1 T Bz k 1 z Bzk 1 T The only open parameter in eq 8 9 is the covariance matrix of the observation noise q This parameter can be estimated by following the standard procedure of LDS parameter estimation via Expectation Maximization EM algorithm D On line Intention Inference and Hand Motion Prediction In the off line stage we construct a set of PDMPs for the corresponding tasks and these PDMPs will then be used for on line intention inference and hand motion prediction a Intention Inference The PDMPs are fi rst queried with the observed on going human hand motion Suppose there are M interested tasks and their corresponding PDMPs are already obtained in the off line stage Then we can use the PDMPs to compute the possibility that the on going motion is belonged to a specifi ed task The details of this phase is given in the line 7 14 of Algorithm 1 b Hand Motion Prediction Once the human intention is inferred this information will be used to determine the attractor of the PDMP to predict future hand motion Before performing the hand motion prediction a new PDMP is constructed as follow ppred k Apred k ppred k 1 Bpredupred 10 qk Cppred k 11 where ppred k x k y k z k p aug k Tis the augmented predic tion state vector which is the cascade of the weights and the augmented state vector Suppose the number of kernel used in each dimension is r then the transition matrix control input matrix control input and the observation matrix of eq 10 11 are Apred k I3r 3rO3r 6 Bask 1Aaug Bpred 03r 3 03 3 t I3 3 upred g C 03r 3 I3 3 03 3 T where g is set equal to the at