Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

Abstract

Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types * This work was supported by the European Commission's Cognition Unit, projects RobotCub and AMARSi. S.G. is funded by a IST-EPFL grant.of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching).

Stable quadruped walking with the adjustment of the center of gravity

“…The phase difference between the oscillator i and the oscillator j can be modulated by extending (1) and (2) [8], shown as follows:…”

“…Another vein of enhancing adaptability is by modifying mathematical model that began by Righetti and Ijspeert. They add the sensory feedback in locomotion generation model such that the model is strongly coupled with the mechanical system it controls [7,8]. Therefore, the presented robot can implement task with desired trajectory.…”

“…The humanoid robot iCub combines rhythmic and discrete motion to turn, drum, reach and crawl [8]. The quadruped robot HyQ utilizes a reactive controller framework to traverse irregular terrain stably [10].…”

Globally stable control of a dynamic bipedal walker using adaptive frequency oscillators

“…An AFO is a nonlinear oscillator that features a learning component to adapt its intrinsic frequency to the frequency of a periodic or quasi-periodic input signal [7]. Control algorithms based on AFO's allow automated, on-line learning and encoding of dynamical movement primitives by a robot [8,4,9]. The encoding of rhythmic movements via dynamical systems not only enables the robot to perform natural, human-like movements, but also allows modulating them in amplitude, frequency or phase by modifying the dynamical system's parameters [10,11].…”

Learning robot gait stability using neural networks as sensory feedback function for Central Pattern Generators

“…In [11], the oscillators are decoupled and feedback from force sensors actually generates the interlimb coordination. In [7], two very basic feedback loops implementing contact feedback and phase resetting for swing stance transitions have shown to increase the robot stability on slopes.…”