We present a general approach to design modular controllers for limit cycle locomotion over unperceived rough terrain. The control strategy uses a Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators as basis. Stumbling correction and leg extension reflexes are implemented as feedbacks for fast corrections, and model-based posture control mechanisms define feedbacks for continuous corrections. The control strategy is validated on a detailed physics-based simulated model of a compliant quadruped robot, the Oncilla robot. We demonstrate dynamic locomotion with a speed of more than 1.5 BodyLength/s over unperceived uneven terrains, steps, and slopes.

In this paper we present a framework to learn a model-free feedback controller for locomotion and balance control of a compliant quadruped robot walking on rough terrain. Having designed an open-loop gait encoded in a Central Pattern Generator (CPG), we use a neural network to represent sensory feedback inside the CPG dynamics. This neural network accepts sensory inputs from a gyroscope or a camera, and its weights are learned using Particle Swarm Optimization (unsupervised learning). We show with a simulated compliant quadruped robot that our controller can perform significantly better than the open-loop one on slopes and randomized height maps.

In this paper we present an approach to the problem of stabilizating the gaze of legged robots using Adaptive Frequency Oscillators to learn the frequency, phase and amplitude of the optical flow and generate compensatory commands during robot locomotion. Assuming periodic and nearly sine shaped motion of the head of the robot, the system successfully stabilizes the gaze of the robot, whether the robot itself is moving, or an external object is moving relative to the robot. We present experiments in simulation and, for object tracking, with a real robotics setup, the Hoap 3, showing that the system can be successfully applied to gaze stabilization during locomotion, even when the feedback loop is very slow and noisy.

In this paper we present our work on integrating a locomotion controller based on central pattern generator (CPG) and a motion planning algorithm using artificial potential fields for a non-holonomic crawling humanoid robot, the iCub. We also integrated a vision tracker and an inverse kinematics solver to perform reaching tasks. We study the influence of the various parameters of the potential field equations on the performance of the system and prove the efficiency of our framework by testing it on a physics-based robotics simulator and partially on the real iCub.