Abstract-Resolved acceleration control is a well-known strategy used in tracking control of robotic systems where the desired motion is specified in task-space. Typically, such controllers are developed for systems which exhibit redundancy with respect to execution of operational tasks. While redundancy fundamentally adds new capabilities (self-motion and subtask performance capability), the degree to which secondary objectives can be faithfully executed cannot be determined in advance unless the motion is planned and the environment is known. Therefore, execution of secondary objectives cannot be guaranteed. In fact, a robot which exhibits redundancy with respect to operational tasks may have insufficient degrees of freedom to fulfill more critical objectives such as enforcing constraints. In this paper, we present a generalized constrained resolved acceleration control framework to handle execution of operational tasks and constraints for redundant and nonredundant task (and constraint) specifications. The approach is particularly well suited for online control of complex robot structures such as humanoid robots. The current formulation considers joint limit and collision constraints. The efficacy of the proposed algorithm is demonstrated by simulated experiments of task level upper-body human motion replication on the Honda humanoid robot.

The force distribution problem (FDP) in robotics requires the determination of multiple contact forces to match a desired net contact wrench. For the double support case encountered in humanoids, this problem is underspecified, and provides the opportunity to optimize desired foot centers of pressure (CoPs) and forces. In different contexts, we may seek CoPs and contact forces that optimize actuator effort or decrease the tendency for foot roll. In this work, we present two formulations of the FDP for humanoids in double support, and propose objective functions within a general framework to address the variety of competing requirements for the realization of balance. As a key feature, the framework is capable to optimize contact forces for motions on uneven terrain. Solutions for the formulations developed are obtained with a commercial nonlinear optimization package and through analytical approaches on a simplified problem. Results are shown for a highly dynamic whole-body humanoid reaching motion performed on even terrain and on a ramp. A convex formulation of the FDP provides real-time solutions with computation times of a few milliseconds. While the convex formulation does not include CoPs explicitly as optimization variables, a novel objective function is developed which penalizes foot CoP solutions that approach the foot boundaries.

This paper introduces a very efficient, modified resolved acceleration control algorithm for dynamic filtering and control of whole-body humanoid motion in response to upper-body task specifications. The dynamic filter is applicable for general upper-body motions when standing in place. It is characterized by modification of the commanded torso acceleration based on a geometric solution to produce a ZMP which is inside the support. The resulting feasible modified motion is synchronized to the reference motion when the computed ZMP for the reference motion again falls within the support. Contact forces at each foot are controlled through a dedicated force distribution module which optimizes the ankle roll and pitch torques. The proposed approach uses time-local information and is therefore targeted for online control. The effectiveness of the algorithm is demonstrated by means of simulated experiments on a model of the Honda humanoid robot ASIMO using a highly dynamic upper-body reference motion.

Human-to-humanoid motion retargeting is an important tool to generate human-like humanoid motions. This retargeting problem is often formulated as a Cartesian control problem for the humanoid from a set of task points in the captured human data. Classically, Cartesian control has been developed for redundant systems. While redundancy fundamentally adds new sub-task capabilities, the degree to which secondary objectives can be faithfully executed cannot be determined in advance. In fact, a robot that exhibits redundancy with respect to an operational task may have insufficient degrees of freedom (DOFs) to satisfy more critical constraints. In this paper, we present a Cartesian space resolved acceleration control framework to handle execution of operational tasks and constraints for redundant and nonredundant task specifications. The approach is well suited for online control of humanoid robots from captured human motion data expressed by Cartesian variables. The current formulation enforces kinematic constraints such as joint limits, self-collisions, and foot constraints and incorporates a dynamically-consistent redundancy resolution approach to minimize costly joint motions. The efficacy of the proposed algorithm is demonstrated by simulated and real-time experiments of human motion replication on a Honda humanoid robot model. The algorithm closely tracks all input motions while smoothly and automatically transitioning between regimes where different constraints are binding.