论文2G精华版本 ICRA 2018 files 0006

Adapting Parameterized Motions using Iterative Learning and Online Collision Detection Johan S Laursen Lars C S rensen Ulrik P Schultz Lars Peter Ellekilde and Dirk Kraft Abstract Achieving both the fl exibility and robustness re quired to advance the use of robotics in small and medium sized productions is an essential but diffi cult task A fundamental problem is making the robot run blindly without additional sensors while still being robust to uncertainties and variations in the assembly processes In this paper we address the use of parameterized motions suitable for blind execution and robust to uncertainties in the assembly process Collisions and incorrect assemblies are detected based on robot motor currents while motion param eters are updated based on Bayesian Optimization utilizing Gaussian Process learning This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups The result is a simple and straightforward system which helps the user automatically fi nd robust and uncertainty tolerant motions We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system I INTRODUCTION To enable greater penetration of robots into small and medium sized enterprises SMEs is considered an important task 1 2 As such more fl exible robot solutions are called for which allows for SME like manufacturing with varied tasks small batches and low volume productions to be automated using robots Creating robot setups is however typically complicated and time consuming Moreover such assembly setups need to be robust to uncertainties and variations inherent in the process This robustness can be achieved by adding and fusing sensors as demonstrated by J org et al 3 or through force torque sensors as e g shown by Chen et al 4 However adding additional sensors is usually expensive and increases the complexity of the setup In this paper we present a system for generating trajecto ries suitable for blind execution The system optimizes mo tion parameters for robustness using only the robots internal sensors during a learning phase Learning is performed on a physical robot platform where collisions occurring during incorrect assemblies are detected based on abnormalities in robot motor currents The system eliminates the need for additional sensors and does not require a highly calibrated workcell model since adjustments are handled in the real world learning process This paper s novelty is a system which uses simple collision detection and a suitable learning This work was supported by The Danish Council for Strategic Research through the CARMEN project Johan Sund Laursen Lars C S rensen Ulrik Pagh Schultz Lars Peter Ellekilde and Dirk Kraft are with The Maersk Mc Kinney Moller Institute Faculty of Engineering University of Southern Denmark Denmark Email josl lcs ups lpe kraft mmmi sdu dk Fig 1 The overall concept for fi nding optimized parameters for robot trajectories in robotic assembly The system is based on iterative learning and feedback in the form of collisions detected based on robot motor currents strategy to create such robust and uncertainty tolerant trajec tories based on real experiments Moreover we introduce a new acquisition function for Bayesian Optimization suitable for binomial classifi cation and a concept for robot collision detection using only motor current sensors Fig 1 shows an overview of the system As input the system takes a parameterized motion Initially this trajectory is executed without collisions Robot motor currents are recorded and an expectation baseline to be used by the collision detection is formed Hereafter the system itera tively searches for robust variation of the trajectory which complies with uncertainties and variations in the assembly process Hence the learning adapts the motion parameters based on feedback from the collision detector regarding the successfulness of the assemblies As learning method we apply Bayesian Optimization utilizing Gaussian Process The rest of this paper is organized as follows Sec II moti vates our work and discusses related work Sec III presents the collision detection mechanic and Sec IV presents the adaptive learning method Last Sec V documents our ex periments and Sec VI concludes and summarized the paper II BACKGROUND ANDRELATEDWORK Having the robot run blindly without additional sensors while still being robust is at the core of our system design The related work is organized as follows Overall our sys tem addresses challenges associated with robotic assembly Sec II A Using a combination of learning and collision detection we suggest a simple system for creating robust 2018 IEEE International Conference on Robotics and Automation ICRA May 21 25 2018 Brisbane Australia 978 1 5386 3080 8 18 31 00 2018 IEEE7587 parameterized robot motions in the real world Errors and faulty assemblies are detected based on robot motor currents Sec II B and used as feedback to the learning system which continuously optimizes the motion parameters Sec II C A Robotic Assembly As an input the system takes a parameterized motion In our concrete case these parameterized motions comes from a library where we use dynamic simulation and uncertainty models to generate an optimized parameter set which makes the trajectory uncertainty tolerant 5 However going from simulation to the physical world often introduces uncertain ties because of aspects not modeled in the simulation Our system provides a simple way of extending the learning to the physical world to further improve the trajectories These parametrized motions could also come from other sources e g Dynamic Movement Primitives DMPs 6 or skill libraries such as those proposed by Thomas et al 7 B gh et al 8 and Huckaby et al 9 which are also com mon suggestions for making robot assembly programming easier Passive and active compliance can both be used to increase the robustness of assembly Active compliance offers the possibility of making advanced controlled contact motions under uncertainty but at the cost of increased complexity This complexity is evident in programming languages e g as presented by Klotzb ucher et al 10 Passive compliance is in comparison to active compliance inexpensive less complex in use and tends allows faster robot motions but is also infl exible 11 Passive compliance can increase the robustness of some robot assembly operations and is in this paper applied B Error Detection and Contact Estimation Our collision detection system is inspired by Chris tensen et al 12 Tarapore et al 13 and Canham et al 14 which are generic error detection solutions inspired by the biological immune system They assume faults changes the fl ow of sensory data These systems can be trained to recognize abnormalities using learning and pattern recogni tion meaning this eliminates the need for users to specify error conditions manually The collision detection system presented in this paper is designed specifi cally for robot assembly where issues such as reaction time and sampling frequency make these generic solutions diffi cult to use However the base principle of detecting abnormalities in the sensory data remains the same Sensorless force estimation based on robot motor torques calculated based on friction models and robot motor currents was demonstrated by Stolt 15 Similar Colom e et al 16 shows how external forces can be estimated for complaint robot manipulation Magrini et al 17 and Lee et al 18 use sensorless force estimation and collision detection for safe human robot collaboration However estimation accuracy of these systems is dependent on having a detailed and comprehensive model of the robot kinematics and mass distribution Berger et al 19 uses a machine learning approach where the robot is trained with an actual force sensor for a given assembly task During the training the robots internal sensors are recorded to create a statistical model which can later be used to estimated forces for similar tasks The approach by Berger et al allows for sensor fusion and multiple sensors being used at the same time as well as for continuous feedback 20 We employ similar ideas to that proposed by Berger et al but can forgo much of the complexity as we do not require an actual force estimation to detect collisions C Learning for Local Adaptation Local motion parameter adaptation is at the core of our problem and the robotics community has dealt with these local learning problems in multiple ways Policy Search PS methods 21 are often preferred in robotics since they locally adapt a policy from a starting point to maximize the reward outcome tied to successful evaluations PS methods have the advantage of scaling well to high dimensional problems The typical approach for PS is to estimate a current best policy iteratively and to add small perturbations to this policy to allow for exploration The estimation of the current best policy uses methods such as gradient descent expectation maximization or evolution ary algorithms Especially the sampling technique typically based on small perturbations 21 is important when work ing with time consuming real world experiments Because our collision detection can handle trajectories that lead to failures we do not need to limit ourselves to use small perturbations of the current best policy Moreover we want to exploit the uncertainty about the promising regions of the parameter space when selecting our next sample As a result of this and because of the sampling technique we will not limit ourselves to this local methods As function predictor Gaussian Process GP 22 has caught increasing interest recently where it has been used for solving both regression GPR and classifi cation GPC problems Bischoff et al 23 uses GPR to incorporate additional knowledge into the model by the prior distribution Berczi et al 24 used GPC to classify where the terrain was traversable for a mobile robot Moreover Berczi et al showed that GPC outperforms Support Vector Machines SVM regarding both classifi cation accuracy and uncertainty estimation for their problem Bayesian Optimization BO 25 is a preferable iterative method when the objective is to optimize an expensive cost function which entails that the sampling technique has a high impact on the performance Commonly GP is utilized as the function predictor for the BO as in Calandra et al 26 where BO and GPR are used for learning gaits for a bipedal robot Tesch et al 27 combined BO with GRC for learning a robot snake to climb over obstacles Our approach is based on BO with GPC However in contrast to the methods described here we have modifi ed the BO acquisition function to better fi t the characteristics of the binomial classifi cation problem 7588 III FAILUREDETECTIONBASED ONMOTORCURRENTS Our system takes a parametrized motion as its starting point for both the learning and collision detection Using simulation an initial guess for parameters has been found Our collisions detection system is based on identifying abnormalities in robot motor currents Detecting such ab normalities becomes complicated due to noise in the motor currents which combined with a low sample frequency makes it diffi cult to react fast enough for the robot to stop before the collision causes damage or the robots own protective stop kicks in The collision detection part is made for and tested using a UR5 universals robot with a CB3 controller This robot has an update rate of 125Hz which in general restricts the robots movement speed when used in a control loop We use a 6D passive compliance device to keep the speed of the robot up while still having time to react to collisions Passive compliance devices are cheap in contrast to force torque sensors they smoothen the forces in a collision and do not complicate the software A Collision Detection Overview Fig 2 shows an overview of the collision detection part of the system Before the collision system can be used it needs to run an initialization procedure During this procedure the robot performs an initial execution of the parametrized motion obtained from simulation This trajectory is also the starting point for the learning process Sec IV The exe cution needs to be collision free and is therefore performed without any workpieces present in the workcell Baseline samples are recorded during this initial execution Each sample consists of a matching pair of robot position and motor currents Collisions are then detected by comparing new samples to the baseline The algorithm for detecting collisions is based on three steps First new samples are matched with a corresponding baseline sample based on robot position Second statistics re garding expected motor current and deviation are calculated Last a decision regarding collision occurrence is made Step 1 Record matching To align a sample with a recorded baseline sample a sliding window algorithm is used This algorithm is used to search for the best matching robot position around the last match A slightly modifi ed version of the sliding window algorithm presented by Iversen et al 28 is used This ensures records are only searched forward in time Samples are matched based on the robots Cartesian position since matching using motor currents alone is too noisy We use the Euclidean distance and a window size of fi ve neighboring samples to search for matches Perturbed versions of the initial trajectory are executed during the learning process We account for the known offset between trajectories to improve the matching of recorded and current positions Step 2 Statistics calculations We defi ne a new sample as s and the robot motor current as I s From the matched baseline sample b s a mean value I b s and standard de viation I b s are calculated based on neighboring baseline Fig 2 Overview of the collision detection part samples The sample error e s is expressed as a ratio e s I s I b s I b s 1 The ratio allows us to express the threshold regarding stan dard deviations This also means the algorithm adapts to changes in variance which for instance occur if the trajectory includes a switch between a vertical to horizontal move We use an unweighted window of size seven baseline samples around b s to calculate I b s and I b s Alternatively several initial baseline records could be used however ex perience shows that the two approaches perform roughly equal while the window requires less initialization We get motor currents from individual joints but use the Euclidean distance to convert this vector into a scalar This suppresses small variations in non moving joints A weighting of the joints could be performed to increase sensitivity but this will introduce additional variables Step 3 Detection algorithm For determining whether to stop the robot and to provide the learning mechanism with the required feedback we convert the values into a binary decision regarding the collision We use both a threshold t and a confi dence bound c to calculate a function h s as described in 2 Collisions are detected and the robot motion is stopped when h s becomes negative The confi dence bound suppresses bias between sample currents and baseline currents The threshold is used for detecting collisions which often become noticeable over the span of one to a few samples as a result of the passive compliance device h 0 0 h s 1ift e s e s h s 1 otherwise 2 In our method we use an empirically found confi dence bound of c 2 5 standard deviations and a threshold of t 14 summed standard deviations If data is assumed to be normal distributed then samples only count towards the threshold if the deviation is within 1 most unlikely samples However with several hundred samples per trajectory such outliers is not uncommon 7589 Fig 3 Illustrations of the robot motor current data for two executions with the collision detection enabled and disabled The baseline in gray with the lower confi dence bound in light gray The light gray area represents a lower triviality limit and not the threshold for collision detection B Example Fig 3 shows a recording of the robot motor currents occurring during a trajectory execution The record is created from a trajectory which moves the robots tool center point vertically down before making a linear horizontal motion hence the signifi cant change in current In the illustration the gray area marks the confi dence bound area calculated based on the initial collision free execution of the trajectory The blue line marks the currents registered where we intentionally blocked the path thereby forcing a crash and used the detection part to stop the robot The red line marks a second execution of the same trajectory but with the detection sys tem deactivated which causes the robot to enter emergency stop IV LEARNING ANDADAPTATION Our system takes in a parameterized motion which then is adapted to comply with the real world During the adaptation process the motion parameters are optimized to ensure robust and reliable performance during real execution As previously described the parameterized motion can be instan tiated by different sources In our case this is done by learn ing in simulation which incorporates different uncertainty sources to account for certain real world variation 5 Hence the learning in simulation makes the motion uncertainty tolerant to the modeled uncertainties However simulation artifacts and calibration issues such as geometric approxi mations and real world fi xture placements can still lead to a non ideal result These can be further accounted for by continuing learning in the real world The objective of the adaptive learning method described in this section is to adjust the input motion by optimizing the success rate and thereby robustness of the motion execution in the real world For this Bayesian Optimization BO 25 based on Gaussian Proces