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This paper presents a standing balance controller that explicitly handles pushes. We employ a library of optimal trajectories and the neighboring optimal control method to generate local approximations to the optimal control. We take advantage of a parametric nonlinear optimization method, SNOPT, to generate initial trajectories and then use Differential Dynamic Programming (DDP) to further refine...
This paper proposes ankle torque and foot placement control methods in the coronal plane (frontal or roll) for a humanoid robot. It is an important problem to control the posture of a humanoid robot in the coronal plane during walking. When the humanoid robot walks, the phase portrait of the pelvis position in the coronal makes an elliptical shape. In this paper, we develop an ankle torque and foot...
We propose using the kernel dimension reduction (KDR) to extract a low-dimensional feature space for humanoid locomotion tasks. Although humanoids have many degrees of freedom, task relevant feature spaces can be much smaller than the number of dimension of the original state space. We consider an application of the proposed approach to improve the locomotive performance of humanoid robots using an...
Multiple strategies for standing balance have been observed in humans, including using the ankles to apply torque to the ground, using the hips and/or arms to generate horizontal ground forces, and using the knees and hips to squat. This paper shows that multiple strategies can arise from the same optimization criterion. It is likely that humanoid robots will exhibit the same balance strategies as...
This paper presents an analysis of a hydraulic joint on a humanoid robot. Various controllers have been designed that allow the limb to have a range of characteristics such as being stiff or compliant.
This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of a slower actuator time constant and the back-drivability of hydraulic joints...
Developing global policies for humanoid robots using dynamic programming is difficult because they have many degrees of freedom. We present a formalism whereby a value function for a humanoid robot can be approximated using the known value functions of similar systems. These similar systems can include approximate models of the robot with reduced dimensionality or trajectories derived from human motion...
We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although RL is a useful non-linear optimizer, it is usually difficult to apply RL to real robotic systems - due to the large number of iterations required to acquire suitable policies. In this study, we first approximated the dynamics by using...
We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center of pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We applied the proposed control approach to our newly developed human...
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