Torque-controlled Humanoid Robot Research Articles

High-precision dynamic torque control of high stiffness actuator for humanoids

The high stiffness actuator (HSA), applied to each joint of an electrical driven humanoid robot, can directly affect the motion performance of the torque-controlled humanoid robots. For high control performance of HSA, a high-precision dynamic torque control (HDTC) is proposed. The HDTC consists of two phases: (1) A novel dynamic current control is used to linearize high stiffness actuator torque control system, which can estimate and compensate the nonlinear coupling parts; (2) An enhanced internal model control is designed to ensure high tracking accuracy in the system containing noisy torque signal and even numerical differentiation signals. Benefitting from dynamic current control and the enhanced internal model control, the proposed HDTC is accurate and adaptable. Finally, the superiority of the HDTC is verified with comparative experiments.

Multi-contact planning and control for humanoid robots: Design and validation of a complete framework

In this paper, we consider the problem of generating appropriate motions for a torque-controlled humanoid robot that is assigned a multi-contact loco-manipulation task, i.e., a task that requires the robot to move within the environment by repeatedly establishing and breaking multiple, non-coplanar contacts. To this end, we present a complete multi-contact planning and control framework for multi-limbed robotic systems, such as humanoids. The planning layer works offline and consists of two sequential modules: first, a stance planner computes a sequence of feasible contact combinations; then, a whole-body planner finds the sequence of collision-free humanoid motions that realize them while respecting the physical limitations of the robot. For the challenging problem posed by the first stage, we propose a novel randomized approach that does not require the specification of pre-designed potential contacts or any kind of pre-computation. The control layer produces online torque commands that enable the humanoid to execute the planned motions while guaranteeing closed-loop balance. It relies on two modules, i.e., the stance switching and reactive balancing module; their combined action allows it to withstand possible execution inaccuracies, external disturbances, and modeling uncertainties. Numerical and experimental results obtained on COMAN+, a torque-controlled humanoid robot designed at Istituto Italiano di Tecnologia, validate our framework for loco-manipulation tasks of different complexity.

Passive Inverse Dynamics Control Using a Global Energy Tank for Torque-Controlled Humanoid Robots in Multi-Contact

This letter presents a passivity-based inverse dynamics (ID) controller using a global energy tank. The proposed control approach allows us to achieve a safe multi-contact scenario on a torque controlled humanoid robot. The controller is primarily a task space ID quadratic programming (QP) which efficiently computes the reference torque satisfying a non-hierarchical set of tasks. Our work extends this controller by adding a global energy tank modulating the task gains, with power regulation, to ensure the passivity of the system. This method combines the benefits of the ID controller, which computes an optimal reference without joint torque feedback, and of the passivity-based system, which is robust to model uncertainties and external disturbances. The robustness of our framework is demonstrated in Gazebo simulations, where the robot TALOS achieves a multi-contact scenario and a 20 cm step walk, with objectives in the Cartesian and configuration spaces, in torque control. The implementation of this controller is open-source.

Emergent Humanoid Robot Motion Synergies Derived From the Momentum Equilibrium Principle and the Distribution of Momentum

The momentum equilibrium principle reveals the relative character of the spatial momentum equation in floating-base robotics. This work clarifies how to take advantage of this character in motion generation and the design of a multitask controller for a humanoid robot. We focus especially on the angular momentum of the robot and the inherent redundancy resolution problem referred to as the “momentum distribution problem.” It is shown that with a proper momentum distribution, emergent behaviors (motion synergies) can be obtained that resemble those used by humans. A real-time controller for position- and torque-controlled humanoid robots is proposed, which has a simple structure. The performance of the controller is confirmed in a simulated environment with a number of tasks, including reactive motion control to accommodate external disturbances (both continuous-force and impacts), acrobatic tasks such as somersaults and jumps, dynamic walking with various gaits and variable center of mass height, blind stepping on an unknown obstacle, and balancing on a wobble board under external disturbances.

A Benchmarking of DCM-Based Architectures for Position, Velocity and Torque-Controlled Humanoid Robots

This paper contributes toward the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body quadratic programming (QP) control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372[Formula: see text]m/s, which is the highest walking velocity achieved by the iCub robot.

Passivity Analysis and Control of Humanoid Robots on Movable Ground

Reliable and robust balancing controllers are key elements of whole-body control frameworks for humanoid robots. Passivity-based balancing controllers have been proposed and tested successfully in various scenarios involving different types of ground surfaces. This letter extends the passivity considerations for covering not only the robot and the controller but also the ground floor in order to guarantee an appropriate behavior, even when the underlying ground surface is not stable. The proposed extension is validated with experiments using the humanoid robot TOrque-controlled humanoid RObot (TORO).

Passivity-based whole-body balancing for torque-controlled humanoid robots in multi-contact scenarios

This work presents a new control approach to multi-contact balancing for torque-controlled humanoid robots. The controller includes a non-strict task hierarchy, which allows the robot to use a subset of its end effectors for balancing while the remaining ones can be used for interacting with the environment. The controller creates a passive and compliant behavior for regulating the center of mass (CoM) location, hip orientation and the poses of each end effector assigned to the interaction task. This is achieved by applying a suitable wrench (force and torque) at each one of the end effectors used for interaction. The contact wrenches at the balancing end effectors are chosen such that the sum of the balancing and interaction wrenches produce the desired wrench at the CoM. The algorithm requires the solution of an optimization problem, which distributes the CoM wrench to the end effectors taking into account constraints for unilaterality, friction and position of the center of pressure. Furthermore, the feedback controller is combined with a feedforward control in order to improve performance while tracking a predefined trajectory, leading to a control structure similar to a PD+ control. The controller is evaluated in several experiments with the humanoid robot TORO.

Good Posture, Good Balance: Comparison of Bioinspired and Model-Based Approaches for Posture Control of Humanoid Robots

This article provides a theoretical and thorough experimental comparison of two distinct posture control approaches: 1) a fully model-based control approach and 2) a biologically inspired approach derived from human observations. While the robotic approach can easily be applied to balancing in three-dimensional (3-D) and multicontact (MC) situations, the biologically inspired balancer currently only works in two-dimensional situations but shows interesting robustness properties under time delays in the feedback loop. This is an important feature when considering the signal transmission and processing properties in the human sensorimotor system. Both controllers were evaluated in a series of experiments with a torque-controlled humanoid robot (TORO). This article concludes with some suggestions for the improvement of model-based balancing approaches in robotics.