Articulated Soft Robot Control: Nonlinear elastic resonance modes for efficient robot and biological locomotion

Abstract

Controlling motion robustly and at low energetic cost, both from mechanical and computational point of view, certainly constitutes one of the major locomotion challenges in biology and robotics. We attempt to demonstrate that robots can be designed and controlled to walk highly efficient by exploiting resonance body effects, increasing the performance compared to rigid body designs. To do so, however, legged robots need to achieve limit cycle motions of the highly coupled, non-linear body dynamics. This led us to the research of the still not very well understood theory of nonlinear system intrinsic modal oscillation control. I will present current theoretical and experimental results therewith. One of the striking results is that biomechanics, in particular kinematics, visco-elastic and inertial properties of biological limbs are such that coordinated resonant motions of multiple joints intrinsically emerges and is therefore easy to excite and sustain. This can be also achieved by careful design for robotic systems. Moreover, I will present a significant extension of our previous work on compliance control of flexible joint robots, which allow implementing all control features of the DLR-light-weight robots also on highly, elastic, Variable Compliance Robots, such as the DLR humanoid David. Some of the basic robotics control functions we developed for locomotion strikingly resemble neural functionalities and structures. For example, Hebbian lerning, one of the most basic principles of synaptic plasticity, is mathematically equivalent to robotic controllers which adapt to previously unknown resonance properties of the body. Based on the robot control approach, we propose an equivalent neural model involving neural plasticity in the spine and the serotonergic loop in the brain-stem. This hypothesis is supported by numerous experimental evidences from neuroscience.