Learning multiple collaborative tasks with a mixture of Interaction Primitives

Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box.

[1]  Aaron F. Bobick,et al.  Probabilistic human action prediction and wait-sensitive planning for responsive human-robot collaboration , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).

[2]  Jan Peters,et al.  Learning responsive robot behavior by imitation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Sandra Hirche,et al.  Feedback motion planning and learning from demonstration in physical robotic assistance: differences and synergies , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Bernhard Schölkopf,et al.  Probabilistic movement modeling for intention inference in human–robot interaction , 2013, Int. J. Robotics Res..

[5]  Oliver Kroemer,et al.  Interaction primitives for human-robot cooperation tasks , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[7]  Hema Swetha Koppula,et al.  Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Yoshihiko Nakamura,et al.  Physical human robot interaction in imitation learning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  H. Harry Asada,et al.  A robot on the shoulder: Coordinated human-wearable robot control using Coloured Petri Nets and Partial Least Squares predictions , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[12]  Minija Tamosiunaite,et al.  Interaction learning for dynamic movement primitives used in cooperative robotic tasks , 2013, Robotics Auton. Syst..

[13]  Carme Torras,et al.  Learning Collaborative Impedance-Based Robot Behaviors , 2013, AAAI.

[14]  Jan Peters,et al.  Learning interaction for collaborative tasks with probabilistic movement primitives , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[15]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interaction , 1999, ICVS.

[17]  K. Kosuge,et al.  Motion planning with worker's trajectory prediction for assembly task partner robot , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Jan Peters,et al.  Probabilistic Movement Primitives , 2013, NIPS.