This paper considers the design of state estimators for dynamic balancing systems using a Linear Inverted Pendulum model with unknown modeling errors such as a center of mass measurement offset or an external force. A variety of process and output models are constructed and compared. For a system containing modeling error, it is shown that a naive estimator (one that doesn't account for this error) will result in inaccurate state estimates. These state estimators are evaluated on a force-controlled humanoid robot for a sinusoidal swaying task and a forward push recovery task.

This paper presents a model-based method, called Dynamic Balance Force Control (DBFC), for determining full body joint torques based on desired COM motion and contact forces for compliant humanoid robots. The center of mass (COM) dynamics are affected directly through contact force control to achieve stable balance. This idea is used to formulate DBFC considering the full rigid-body dynamics of the robot to produce desired contact forces. To achieve generic force control tasks, a virtual model controller, DBFC-VMC, is presented. Results presented from experiments on a force-controlled humanoid robot and simulation demonstrate the general purpose use of this control.

This paper develops full-state parametric controllers for standing balance of humanoid robots in response to impulsive and constant pushes. We also explore a hypothesis that postural feedback gains in standing balance should change with perturbation size. From an engineering point of view this is known as gain scheduling. We use an optimization approach to see if feedback gains should scale with the perturbation for a simulated robot. We simulate models in the sagittal and lateral plane and in 3-dimensions, use a horizontal push of a given size, direction and location as a perturbation, and optimize parametric controllers for different push sizes, directions and locations. During a simulated perturbation experiment, the appropriate controller is continuously selected based on the current push. For an impulse, the simulated robot recovers back to the initial state; for a constant push, the robot moves to an equilibrium position which leans into the push and has zero joint torques. We show the performance of optimized parametric controllers in response to different external pushes.

We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using local trajectory optimizers to globally optimize a policy and associated value function. Our focus is on finding steady-state policies for deterministic time-invariant discrete time control problems with continuous states and actions often found in robotics. In this paper, we describe our approach and provide initial results on several simulated robotics problems.

This paper presents a balance controller that allows a humanoid to recover from large disturbances and still maintain an upright posture. Balance is achieved by integral control, which decouples the dynamics and produces smooth torque signals. Simulation shows the controller performs better than other simple balance controllers. Because the controller is inspired by human balance strategies, we compare human motion capture and force plate data to simulation. A model tracking controller is also presented, making it possible to control complex robots using this simple control.

We combine two threads of research on approximate dynamic programming: random sampling of states and using local trajectory optimizers to globally optimize a policy and associated value function. This combination allows us to replace a dense multidimensional grid with a much sparser adaptive sampling of states. Our focus is on finding steady state policies for the deterministic time invariant discrete time control problems with continuous states and actions often found in robotics. In this paper we show that we can now solve problems we couldn't solve previously with regular grid-based approaches.

We study the problem of generating motion plans for kinematically controllable underactuated systems in environments cluttered with obstacles. We develop a computationally efficient motion planning algorithm that finds fast trajectories by exploiting closed-form inverse kinematics of the robot. The completeness property of the motion planning algorithm can be proven using appropriate metrics defined in the configuration space of the kinematically controllable systems. The snakeboard is used as an example of a kinematically controllable underactuated system to test the motion planning algorithm, and motion plans have been implemented on an experimental snakeboard.