227 research outputs found
Lower body design of the âiCubâ a human-baby like crawling robot
The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented
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).Comment: Pre-print of paper presented at Humanoid Robots, 13th IEEE-RAS
International Conference on, Atlanta, Georgia, 201
Improving Dynamics Estimations and Low Level Torque Control Through Inertial Sensing
In 1996, professors J. Edward Colgate and Michael Peshkin invented the
cobots as robotic equipment safe enough for interacting with human workers.
Twenty years later, collaborative robots are highly demanded in the
packaging industry, and have already been massively adopted by companies
facing issues for meeting customer demands. Meantime, cobots are still
making they way into environments where value-added tasks require more
complex interactions between robots and human operators. For other applications
like a rescue mission in a disaster scenario, robots have to deal with
highly dynamic environments and uneven terrains. All these applications
require robust, fine and fast control of the interaction forces, specially in the
case of locomotion on uneven terrains in an environment where unexpected
events can occur. Such interaction forces can only be modulated through the
control of joint internal torques in the case of under-actuated systems which
is typically the case of mobile robots. For that purpose, an efficient low level
joint torque control is one of the critical requirements, and motivated the
research presented here. This thesis addresses a thorough model analysis of
a typical low level joint actuation sub-system, powered by a Brushless DC
motor and suitable for torque control. It then proposes procedure improvements
in the identification of model parameters, particularly challenging in
the case of coupled joints, in view of improving their control. Along with
these procedures, it proposes novel methods for the calibration of inertial
sensors, as well as the use of such sensors in the estimation of joint torques
Design and development of robust hands for humanoid robots
Design and development of robust hands for humanoid robot
Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control
As robotic technology advances, the barriers to the coexistence of humans and robots are slowly coming down. Application domains like elderly care, collaborative manufacturing, collaborative manipulation, etc., are considered the need of the hour, and progress in robotics holds the potential to address many societal challenges. The future socio-technical systems constitute of blended workforce with a symbiotic relationship between human and robot partners working collaboratively. This thesis attempts to address some of the research challenges in enabling human-robot collaboration. In particular, the challenge of a holistic perception of a human partner to continuously communicate his intentions and needs in real-time to a robot partner is crucial for the successful realization of a collaborative task. Towards that end, we present a holistic human perception framework for real-time monitoring of whole-body human motion and dynamics. On the other hand, the challenge of leveraging assistance from a human partner will lead to improved human-robot collaboration. In this direction, we attempt at methodically defining what constitutes assistance from a human partner and propose partner-aware robot control strategies to endow robots with the capacity to meaningfully engage in a collaborative task
The CoDyCo Project achievements and beyond: Towards Human Aware Whole-body Controllers for Physical Human Robot Interaction
International audienceThe success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Regarding the former, to properly manage interaction dynamics on the robot control side, an estimation of the human behaviours and intentions is necessary. In this paper we present the building blocks of such a human-in-the-loop controller, and validate them in both simulation and on the iCub humanoid robot using a human-robot interaction scenario. In this scenario, a human assists the robot in standing up from being seated on a bench
Methods to improve the coping capacities of whole-body controllers for humanoid robots
Current applications for humanoid robotics require autonomy in an environment specifically
adapted to humans, and safe coexistence with people. Whole-body control is
promising in this sense, having shown to successfully achieve locomotion and manipulation
tasks. However, robustness remains an issue: whole-body controllers can still
hardly cope with unexpected disturbances, with changes in working conditions, or
with performing a variety of tasks, without human intervention. In this thesis, we
explore how whole-body control approaches can be designed to address these issues.
Based on whole-body control, contributions have been developed along three main
axes: joint limit avoidance, automatic parameter tuning, and generalizing whole-body
motions achieved by a controller. We first establish a whole-body torque-controller
for the iCub, based on the stack-of-tasks approach and proposed feedback control
laws in SE(3). From there, we develop a novel, theoretically guaranteed joint limit
avoidance technique for torque-control, through a parametrization of the feasible joint
space. This technique allows the robot to remain compliant, while resisting external
perturbations that push joints closer to their limits, as demonstrated with experiments
in simulation and with the real robot. Then, we focus on the issue of automatically
tuning parameters of the controller, in order to improve its behavior across different
situations. We show that our approach for learning task priorities, combining domain
randomization and carefully selected fitness functions, allows the successful transfer of
results between platforms subjected to different working conditions. Following these
results, we then propose a controller which allows for generic, complex whole-body
motions through real-time teleoperation. This approach is notably verified on the robot
to follow generic movements of the teleoperator while in double support, as well as to
follow the teleoperator\u2019s upper-body movements while walking with footsteps adapted
from the teleoperator\u2019s footsteps. The approaches proposed in this thesis therefore
improve the capability of whole-body controllers to cope with external disturbances,
different working conditions and generic whole-body motions
Passive Motion Paradigm: An Alternative to Optimal Control
In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the âdegrees of freedom (DoFs) problem,â the common core of production, observation, reasoning, and learning of âactions.â OCT, directly derived from engineering design techniques of control systems quantifies task goals as âcost functionsâ and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative âsofterâ approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that âanimatesâ the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints âat runtime,â hence solving the âDoFs problemâ without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of âpotential actions.â In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures
- âŠ