A Muscle-Reflex Model of Forelimb and Hindlimb of Felidae Family of Animal with Dynamic Pattern Formation Stimuli

Human and animal locomotion are controlled by complex neural circuits, which can also serve as inspiration for designing locomotion controllers for dynamic locomotion in legged robots. We develop a locomotion controller model including a central pattern generator (CPGs) and a muscle reflex based on the forelimb and hindlimb structures of a cat. In this paper, we focus on modeling the muscle reflex and its optimization. This muscle reflex model regulates ground force afferents in each limb. There are two phases in each step performed by this model, the swing and stance phases. The muscle during swing phase is activated by a pattern formation signal from the CPG. During stance phase, the muscle is automatically controlled by the moving speed. We utilize a multi-objective evolutionary algorithm to optimize parameters of the model. We use the proposed model to control a cat-like robot in simulations using Open Dynamics Engine. Results show that the simulated robot is able to move at different speeds by modulating simple stimulation signals to the CPG without needing to modify muscle and reflex parameters.

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis.

EPFL2017

Trot Gait Locomotion of A Cat Sized Quadruped Robot

Mostafa Ajallooeian, Auke Ijspeert, Alexander Spröwitz, Alexandre Tuleu

We are proposing and testing two model-free approaches for locomotion control of a light-weight, compliant, quadruped robot: open loop central pattern generators (CPG), and open and closed- loop dynamical movement primitives (DMP). We are presenting two different knee joint controllers, based on the hypothesis that the passive- compliant leg design might require less control effort for the knee joint control. CPG-control parameters are optimized applying extensive optimization runs using PSO, resulting trot gaits are evaluated for speed and robustness. One derived gait is selected as learning input for a gait controller based on DMP. We design the DMP-based trot gait controller both for open and closed-loop control. For the latter we are feeding back gyroscope and touch sensor signals to modulate the core phase pattern. Found CPG based trot gaits are up to 90cm/sec fast (more than 3 BL/sec). The joint controller assisting the passive compliant knee joint shows gaits with higher speed, and larger hip amplitude. Closed- loop DMP based gaits let the robot handle frontal and sideways slopes, step-downs, and random pushes to the robot’s COM more robustly.

2011

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

EPFL2017