Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

Abstract

Abstract-The framework of dynamic movement primitives contains many favorable properties for the execution of robotic trajectories, such as indirect dependency on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feedforward fashion and thus modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of dynamic movement primitives, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task.

Learning Physical Collaborative Robot Behaviors From Human Demonstrations

“…In [28], the learning problem was treated as that of finding an acceleration-based predictive reaction for coupled agents, in response to force signals indicating disagreements due to obstacle avoidance or different paths to follow. Gams et al [29] argued that such adaptation should be done not only at acceleration, but also at velocity level, allowing for smoother interactions. Their approach learned coupled DMPs using iterative learning control that exploited the force feedback generated during several executions of the task.…”

“…Their approach learned coupled DMPs using iterative learning control that exploited the force feedback generated during several executions of the task. Note that our framework shares similarities with [24], [28], [29] in the sense that interaction forces are considered as additional variables influencing the collaborative robot behavior. Indeed, in our work, these forces affect not only the robot motion, but also its time-varying impedance.…”

Learning from demonstration for semi-autonomous teleoperation

“…Gams et al (2014) extends the DMP framework by adding a modulation term that allows interaction with objects and the environment. Learning of this coupling term is performed with an iterative learning control algorithm.…”

Probabilistic progress prediction and sequencing of concurrent movement primitives

“…Therefore, the parameters of the network had to be defined by hand. A stronger coupling of the concurrent movements can be achieved, for example, by using DMPs and adding a coupling term to the original formulation [7], [8], [9]. In general, such a coupling term allows for conditioning a DMP on external signals.…”