Informes de actuación

The children’s motor skill game Ball-in-a-Cup [Sumners, 1997], also known as Balero, Bilboquet, and Kendama, is challenging even for adults. The toy has a small cup which is held in one hand (or, in our case, is attached to the end-effector of the robot) and the cup has a small ball hanging down on a string (the string has a length of 40cm in our setup). Initially, the ball is hanging down vertically in a rest position. The player needs to move fast in order to induce a motion in the ball through the string, toss it up, and catch it with the cup. A possible movement is illustrated in Figure 4.11 in the top row.

Note that learning of Ball-in-a-Cup and Kendama has previously been studied in robotics. We are going to contrast a few of the approaches here. While we learn directly in the joint space of the robot, Takenaka [1984] recorded planar human cup movements and determined the required joint movements for a planar, three degree of freedom (DoF) robot, so that it could follow the trajectories while visual feedback was used for error compensation. Both Sato et al. [1993] and Shone et al. [2000] used motion planning approaches which relied on very accurate models of the ball and the string while employing only one DoF in [Shone et al., 2000] or two DoF in [Sato et al., 1993] so that the complete state-space could be searched exhaustively. Interestingly, exploratory robot moves were used in [Sato et al., 1993] to estimate the parameters of the employed model. Probably the most advanced preceding work on learning Kendama was carried out by Miyamoto et al. [1996] who used a seven DoF anthropomorphic arm and recorded human motions to train a neural network to reconstruct via-points. Employing full kinematic knowledge, the authors optimize a desired trajectory.

The state of the system can be described by joint angles and joint velocities of the robot as well as the the Cartesian coordinates and velocities of the ball. The actions are the joint space accelerations where each of the seven joints is driven by a separate motor primitive, but with one common canonical system. The movement uses all seven degrees of freedom and is not in a plane. All motor primitives are perturbed separately but employ the same joint final reward given by

r(t) = ( exp€−α xc− xb
2 − α yc− yb
2Š if t= t_c, 0 otherwise,

where we denote the moment when the ball passes the rim of the cup with a downward direction by t_c, the cup position by[x_c, y_c, z_c] ∈ R3, the ball position by[x_b, y_b, z_b] ∈ R3, and a scaling parameter by α = 100 (see also Figure 4.10). The algorithm is robust to changes of this parameter as long as the reward clearly discriminates good and suboptimal trials. The directional information is necessary as the algorithm

(a)Schematic Drawings of the Ball-in-a-Cup Motion

(b)Kinesthetic Teach-In

(c)Final learned Robot Motion

Figure 4.11: This figure shows schematic drawings of the Ball-in-a-Cup motion (a), the final learned robot motion (c), as well as a kinesthetic teach-in (b). The arrows show the directions of the current movements in that frame. The human cup motion was taught to the robot by imitation learning with 31 parameters per joint for an approximately 3 seconds long trajectory. The robot manages to reproduce the imitated motion quite accurately, but the ball misses the cup by several centimeters. After approximately 75 iterations of our Policy learning by Weighting Exploration with the Returns (PoWER) algorithm the robot has improved its motion so that the ball goes in the cup. Also see Figure 4.12.

0 20 40 60 80 100 0 0.2 0.4 0.6 0.8 1 number of rollouts average return

Figure 4.12: This figure shows the expected return of the learned policy in the Ball-in-a-Cup evaluation averaged over 20 runs.

could otherwise learn to hit the bottom of the cup with the ball. This solution would correspond to a local maximum whose reward is very close to the optimal one, but the policy very far from the optimal one. The reward needs to include a term avoiding this local maximum. PoWER is based on the idea of considering the reward as an improper probability distribution. Transforming the reward using the exponential enforces this constraint. The reward is not only affected by the movements of the cup but foremost by the movements of the ball, which are sensitive to small changes in the cup’s movement. A small perturbation of the initial condition or during the trajectory can change the movement of the ball and hence the outcome of the complete movement. The position of the ball is estimated using a stereo vision system and is needed to determine the reward.

Due to the complexity of the task, Ball-in-a-Cup is a hard motor learning task for children, who usually only succeed at it by observing another person playing combined with a lot of improvement by trial- and-error. Mimicking how children learn to play Ball-in-a-Cup, we first initialize the motor primitives by imitation learning and, subsequently, improve them by reinforcement learning. We recorded the motions of a human player by kinesthetic teach-in to obtain an example for imitation as shown in Figure 4.11b. A single demonstration was used for imitation learning. Learning from multiple demonstrations did not improve the performance as the task is sensitive to small differences. As expected, the robot fails to reproduce the presented behavior even if we use all the recorded details for the imitation. Thus, reinforcement learning is needed for self-improvement. The more parameters used for the learning, the slower the convergence is. Due to the imitation, the ball must go above the rim of the cup such that the algorithm gets at least a small positive reward for all rollouts. This way exhaustive exploration is avoided as the algorithm can compare the performance of the different rollouts. We determined that 31 shape-parameters per motor primitive are needed. With less parameters the ball does not go above the rim of the cup in the initial trial and the algorithm does not receive any meaningful information about the trial using the aforementioned reward function. More shape-parameters will lead to a more accurate reproduction of the demonstrated movement and, thus, to a better initial policy. However, there is a trade-off between this better initial policy and a potentially lower learning speed. Using three times as many parameters the algorithm converged at roughly the same time. The hyper-parametersσ_{i j} are initially set in the same order of magnitude as the median of the parameters for each motor primitive and are then optimized alongside the shape-parameters by PoWER. The performance of the algorithm is fairly robust for values chosen in this range. Figure 4.12 shows the expected return over the number of rollouts where convergence to a maximum is clearly recognizable. The robot regularly succeeds at bringing the ball into the cup after approximately 75 iterations. Figure 4.13 shows the improvement of the policy over the rollouts. From our experience, nine year old children get the ball in the cup for the first time after about 35 trials while the robot gets the ball in for the first time after 42-45 rollouts. However, after

Figure 4.13: This figure shows the improvement of the policy over rollouts. The snapshots from the video show the position of the ball closest to the cup during a rollout. (1) Initial policy after imitation learning. (15) Policy after 15 rollouts, already closer. (25) Policy after 25 rollouts, going too far. (45) Policy after 45 rollouts, hitting the near rim. (60) Policy after 60 rollouts, hitting the far rim. (100) Final policy after 100 rollouts.

100 trials, the robot exhibits perfect runs in every single trial while children do not have a comparable success rate. Of course, such a comparison with children is contrived as a robot can precisely reproduce movements unlike any human being, and children can most likely adapt faster to changes in the setup.