Benjamin J. Stephens

Humanoid push recovery

We extend simple models previously developed for humanoids to large push recovery. Using these simple models, we develop analytic decision surfaces that are functions of reference points, such as the center of mass and center of pressure, that predict whether or not a fall is inevitable. We explore three strategies for recovery: 1) using ankle torques, 2) moving internal joints, and 3) taking a step. These models can be used in robot controllers or in analysis of human balance and locomotion.

Dynamic Balance Force Control for compliant humanoid robots

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.

Push Recovery by stepping for humanoid robots with force controlled joints

In order to interact with human environments, humanoid robots require safe and compliant control which can be achieved through force-controlled joints. In this paper, full body step recovery control for robots with force-controlled joints is achieved by adding model-based feed-forward controls. Push Recovery Model Predictive Control (PR-MPC) is presented as a method for generating full-body step recovery motions after a large disturbance. Results are presented from experiments on the Sarcos Primus humanoid robot that uses hydraulic actuators instrumented with force feedback control.

Integral control of humanoid balance

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.

Multiple balance strategies from one optimization criterion

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 humans.

Modeling and control of periodic humanoid balance using the Linear Biped Model

We present work on compliant control of dynamic humanoid balance and walking. We use the Linear Biped Model (LiBM) to model the dynamics of balance on two feet. To achieve periodic motion, as in walking, we derive an orbital energy controller for this model. We also present our methods for applying this control to a torque-controlled humanoid robot, which include estimating the center of mass state and generating feed-forward torque commands.

Torso rotation for push recovery using a simple change of variables

This paper presents a modification for a broad class of controllers based on the LIPM dynamics. We use a change of variables such that instead of controlling the center of mass, we control an “augmented center of mass”, which is unaffected by upper body angular accelerations. We use upper body orientation as an additional source of control authority, allowing us to use both upper body rotation and center of pressure modulation for control. We demonstrate an improved robustness to external pushes with this additional control authority through simulated standing and walking experiments. We also demonstrate the modified controller on our force-controlled humanoid robot.

Gain scheduled control of perturbed standing balance

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.