Andrea Del Prete

iCub Whole-Body Control through Force Regulation on Rigid Non-Coplanar Contacts

This paper details the implementation on the humanoid robot iCub of state-of-the-art algorithms for whole-body control. We regulate the forces between the robot and its surrounding environment to stabilize a desired robot posture. We assume that the forces and torques are exerted on rigid contacts. The validity of this assumption is guaranteed by constraining the contact forces and torques, e.g. the contact forces must belong to the associated friction cones. The implementation of this control strategy requires to estimate the external forces acting on the robot, and the internal joint torques. We then detail algorithms to obtain these estimations when using a robot with an iCub-like sensor set, i.e. distributed six-axis force-torque sensors and whole-body tactile sensors. A general theory for identifying the robot inertial parameters is also presented. From an actuation standpoint, we show how to implement a joint torque control in the case of DC brushless motors. In addition, the coupling mechanism of the iCub torso is investigated. The soundness of the entire control architecture is validated in a real scenario involving the robot iCub balancing and making contacts at both arms.

Skin spatial calibration using force/torque measurements

This paper deals with the problem of estimating the position of tactile elements (i.e. taxels) that are mounted on a robot body part. This problem arises with the adoption of tactile systems with a large number of sensors, and it is particularly critical in those cases in which the system is made of flexible material that is deployed on a curved surface. In this scenario the location of each taxel is partially unknown and difficult to determine manually. Placing the device is in fact an inaccurate procedure that is affected by displacements in both position and orientation. Our approach is based on the idea that it is possible to automatically infer the position of the taxels by measuring the interaction forces exchanged between the sensorized part and the environment. The location of the contact is estimated through force/torque (F/T) measures gathered by a sensor mounted on the kinematic chain of the robot. Our method requires few hypotheses and can be effectively implemented on a real platform, as demonstrated by the experiments with the iCub humanoid robot.

Fast algorithms to test robust static equilibrium for legged robots

Maintaining equilibrium is of primary importance for legged systems. It is not surprising then that static equilibrium is at the core of most control/planning algorithms for legged robots. Being able to check whether a system is in static equilibrium is thus important, and doing it efficiently is crucial. While this is straightforward for a system in contact with a flat ground only, it is not the case for arbitrary contact geometries. In this paper we propose two new techniques to test static equilibrium and we show that they are computationally faster than all other existing methods. Moreover, we address the issue of robustness to errors in the contact-force tracking, which could lead to slippage or rotation at the contacts. We extend all the discussed techniques to test for robust static equilibrium, that is the ability to maintain equilibrium while avoiding to lose contacts despite bounded force-tracking errors. Accounting for robustness does not affect the computation time of the equilibrium tests, while it qualitatively improves the contact postures generated by our reachability-based multicontact planner.

Robustness to Joint-Torque-Tracking Errors in Task-Space Inverse Dynamics

Task-space inverse dynamics (TSID) is a well-known optimization-based technique for the control of highly redundant mechanical systems, such as humanoid robots. One of its main flaws is that it does not take into account any of the uncertainties affecting these systems: poor torque tracking, sensor noises, delays, and model uncertainties. As a consequence, the resulting control-state trajectories may be feasible for the ideal system, but not for the real one. We propose to improve the robustness of TSID by modeling uncertainties in the joint torques, either as Gaussian random variables or as bounded deterministic variables. Then we try to immunize the constraints of the system to any-or at least most-of the realizations of these uncertainties. When the resulting optimization problem is computationally too expensive for online control, we propose ways to approximate it that lead to computation times below 1 ms. Extensive simulations in a realistic environment show that the proposed robust controllers greatly outperform the classic one, even when other unmodeled uncertainties affect the system (e.g., errors in the inertial parameters, delays in the velocity estimates).

Control of contact forces: The role of tactile feedback for contact localization

This paper investigates the role of precise estimation of contact points in force control. This analysis is motivated by scenarios in which robots make contacts, either voluntarily or accidentally, with different parts of their body. Control paradigms that are usually implemented in robots with no tactile system, make the hypothesis that contacts occur at the end-effectors only. In this paper we try to investigate what happens when this assumption is not verified. First we consider a simple feedforward force control law, and then we extend it by introducing a proportional feedback term. For both controllers we find the error in the resulting contact force, that is induced by a hypothetic error in the estimation of the contact point. We show that, depending on the geometry of the contact, incorrect estimation of contact points can induce undesired joint accelerations. We validate the presented analysis with tests on a simulated robot arm. Moreover we consider a complex real world scenario, where most of the assumptions that we make in our analytical derivation do not hold. Through tests on the iCub humanoid robot we see how errors in contact localization affect the performance of a parallel force/position controller. In order to estimate contact points and contact forces on the forearm of the iCub we do not use any model of the environment, but we exploit its 6-axis force/torque sensor and its sensorized skin.

Partial force control of constrained floating-base robots.

Legged robots are typically in rigid contact with the environment at multiple locations, which add a degree of complexity to their control. We present a method to control the motion and a subset of the contact forces of a floating-base robot. We derive a new formulation of the lexicographic optimization problem typically arising in multi-task motion/force control frameworks. The structure of the constraints of the problem (i.e. the dynamics of the robot) allows us to find a sparse analytical solution. This leads to an equivalent optimization with reduced computational complexity, comparable to inverse-dynamics based approaches. At the same time, our method preserves the flexibility of optimization based control frameworks. Simulations were carried out to achieve different multi-contact behaviors on a 23-degree-of-freedom humanoid robot, validating the presented approach. A comparison with another state-of-the-art control technique with similar computational complexity shows the benefits of our controller, which can eliminate force/torque discontinuities.

Implementing Torque Control with High-Ratio Gear Boxes and without Joint-Torque Sensors

This paper presents a complete framework (estimation, identification and control) for the implementation of joint-torque control on the humanoid robot HRP-2. While torque control has already been implemented on a few humanoid robots, this is one of the first implementations of torque control on a robot that was originally built to be position controlled (iCub [F. Nori, S. Traversaro, J. Eljaik, F. Romano, A. Del Prete and D. Pucci, iCub whole-body control through force regulation on rigid non-coplanar contacts, Frontiers in Robotics and AI 2 (2015).] and Asimo [O. Khatib, P. Thaulad and J. Park, Torque-position transformer for task control of position controlled robots, 2008 IEEE Int. Conf. Robotics and Automation, May 2008, pp. 1729–1734.] being the first two, to the best of our knowledge). The challenge comes from both the hardware, which does not include joint-torque sensors and presents large friction due to the high-ratio gear boxes, and the software interface, which only accepts desired joint-angle commands (no motor current/voltage control). The contribution of the paper is to provide a complete methodology that is very likely to be reproduced as most robots are designed to provide only position control capabilities. Additionally, the method is validated by exhaustive experiments on one leg of the robot, including a comparison with the original position controller. We tested the torque controller in both motion control and cartesian force control. The torque control can track better a reference trajectory while using lower values for the feedback gains (up to 25%). Moreover, we verified the quality of the identified motor models by analyzing the contribution of the feedforward terms of our torque controller, which dominate the feedback terms.

Inertial parameter identification including friction and motor dynamics

Identification of inertial parameters is fundamental for the implementation of torque-based control in humanoids. At the same time, good models of friction and actuator dynamics are critical for the low-level control of joint torques. We propose a novel method to identify inertial, friction and motor parameters in a single procedure. The identification exploits the measurements of the PWM of the DC motors and a 6-axis force/torque sensor mounted inside the kinematic chain. The partial least-square (PLS) method is used to perform the regression. We identified the inertial, friction and motor parameters of the right arm of the iCub humanoid robot. We verified that the identified model can accurately predict the force/torque sensor measurements and the motor voltages. Moreover, we compared the identified parameters against the CAD parameters, in the prediction of the force/torque sensor measurements. Finally, we showed that the estimated model can effectively detect external contacts, comparing it against a tactile-based contact detection. The presented approach offers some advantages with respect to other state-of-the-art methods, because of its completeness (i.e. it identifies inertial, friction and motor parameters) and simplicity (only one data collection, with no particular requirements).

High-slope terrain locomotion for torque-controlled quadruped robots

Research into legged robotics is primarily motivated by the prospects of building machines that are able to navigate in challenging and complex environments that are predominantly non-flat. In this context, control of contact forces is fundamental to ensure stable contacts and equilibrium of the robot. In this paper we propose a planning/control framework for quasi-static walking of quadrupedal robots, implemented for a demanding application in which regulation of ground reaction forces is crucial. Experimental results demonstrate that our 75-kg quadruped robot is able to walk inside two high-slope (