Farshad Khadivar

Adaptive Fingers Coordination for Robust Grasp and In-Hand Manipulation Under Disturbances and Unknown Dynamics

We present a control framework for achieving a robust object grasp and manipulation in hand. In-hand manipulation remains a demanding task as the object is never stable and task success relies on carefully synchronizing the fingers' dynamics. Indeed, fingers must simultaneously generate motion while maintaining contact with the object and, by staying within the hand's frame, ensuring that the object remains manipulable. These challenges are exacerbated once the hand gets disturbed or when the internal dynamics of the manipulated object are unknown, such as when it is filled with liquid moving during manipulation. We present a control strategy based on coupled dynamical systems (DSs), whereby the fingers move in synchronization using an intermediate dynamics responsible for coordinating fingers. To adapt to changes in forces due to model uncertainties and unexpected disturbances, we employ an adaptive torque-controller combined with a joint impedance regulator that guarantees high tracking accuracy while adapting to dynamic changes. We validate the approach in multiple experiments on 16-degrees-of-freedom robotic hand grasping and manipulating objects with different mass properties, e.g., uneven or varying mass distribution in a glass half-filled with water. We show that the robot can compensate for disturbances generated by internal dynamics and external perturbations. Additionally, we showcase how our controller, in conjunction with learning from human demonstration, provides a robust solution for more complicated manipulations such as finger gaiting.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Self-Correcting Quadratic Programming-Based Robot Control

Aude Billard, Konstantinos Chatzilygeroudis, Farshad Khadivar

Quadratic Programming (QP)-based controllers allow many robotic systems, such as humanoids, to successfully undertake complex motions and interactions. However, these approaches rely heavily on adequately capturing the underlying model of the environment and the robot's dynamics. This assumption, nevertheless, is rarely satisfied, and we usually turn to well-tuned end-effector PD controllers to compensate for model mismatches. In this paper, we propose to augment traditional QP-based controllers with a learned residual inverse dynamics model and an adaptive control law that adjusts the QP online to account for model uncertainties and unforeseen disturbances. In particular, we propose (i) learning a residual inverse dynamics model using the Gaussian Process and linearizing it so that it can be incorporated inside the QP-control optimization procedure and (ii) a novel combination of adaptive control and QP-based methods to avoid the manual tuning of end-effector PID controllers and faster convergence in learning the residual dynamics model. In simulation, we extensively evaluate our method in several robotic scenarios ranging from a 7-DoFs manipulator tracking a trajectory to a humanoid robot performing a waving motion for which the model used by the controller and the one used in the simulated world do not match (unmodeled dynamics). Finally, we also validate our approach in physical robotic scenarios where a 7-DoFs robotic arm performs tasks where the model of the environment (mass, friction coefficients, etc.) is not fully known.

2023

Advancing the Adaptability of Compliant Robot Controllers for Exploration, Interaction, and Manipulation

Farshad Khadivar

As technology continues to evolve, robots are becoming an intrinsic part of our lives. While robots surpass human capabilities in precision and speed, they are far from matching humans' ability to adapt to unexpected changes. Humans can quickly and compliantly respond to uncertainties and perform complex tasks such as dexterous manipulation. Robots in human-centric environments need to be equipped with human-like capabilities, primarily when such a machine interacts with us or the objects around us. Therefore, the thesis aimed at (i) obtaining a precise controller with human-like compliant capabilities and, with such a controller, (ii) designing a control pipeline to perform dexterous manipulation.Learning, assessing, and constantly updating the underlying robot's dynamics is the first step to obtaining a precise and compliant controller. Accordingly, we present methods to learn and update dynamics models in the first two parts of the thesis. The first part of the thesis investigates supervised machine learning techniques for learning the inverse dynamic model of a robot prior to task execution. We propose a method for incrementally exploring a robot's configuration space and maximizing the information of the collected data.In the second part, we focus on assessing and updating dynamic models during and after task execution. We offer a method that augments episodic model updates with online adaptation. We propose combining traditional model-based controllers with a learned residual inverse dynamics model. Then, we introduce an adaptive control law that adjusts the control reference online to account for model uncertainties and unforeseen disturbances. We dedicate the third part of the thesis to developing a compliant control pipeline to achieve human-like dexterity tasks. We introduce a new robust and synchronized planning schematic for grasping and manipulating tasks. Our approach is to control and synchronize fingers based on dynamical systems. We combine our adaptive controller with joints' impedance regulation to guarantee high tracking accuracy and adapt to dynamic changes. We showcase that in conjunction with learning from human demonstration, our controller provides a robust solution for more complicated manipulations such as finger gaiting.Lastly, we use the developed robotic hand controller in two applications in the human-centric environment. The task in the first application is to increase the dexterity of robotic prosthetic hands (RPHs) for individuals with a hand amputation using a new teleoperated-control scheme via electromyography. In the second application, we perform tactile surface exploration of apriori unknown objects through a novel informative path planning exploration strategy.In summary, this thesis offers a robot controller, compliant in interaction and faithful in tracking, for dexterous grasp and manipulation tasks. Results indicate substantial accuracy improvement over traditional approaches when using our incremental method for configuration exploration. We confirmed by various experiments that our residual model learning procedure could learn unmodeled dynamics in a hand full of trials and adapt online to perturbations in unpredictable environments. Finally, with extensive experiments and two applications, we showed that introducing a coordinated multi-finger system to our controller provides a robust solution for grasping and manipulating problems in uncertain environments.

EPFL2022

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Walid Amanhoud, Aude Billard, Sthithpragya Gupta, Farshad Khadivar

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot's configuration space, in order to collect a rich dataset. This study aims to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques. We propose a method, called Max-Information Configuration Exploration (MICE), to incrementally explore and generate information-rich data via computing parameters of a trajectory set. We also introduce a new set of excitation trajectories that explores robot's configuration through imposed stable limit cycles in robot joints' phase space while satisfying feasibility constraints and physical bounds. We benchmark MICE against state-of-the-art in terms of data quality and learning accuracy. The proposed methodology for data collection, model learning, and evaluation, is validated with a KUKA IIWA14 robotic arm where the results prove significant improvement over traditional approaches.

IEEE2021

Benchmark for Bimanual Robotic Manipulation of Semi-deformable Objects

Aude Billard, Fanjun Bu, Konstantinos Chatzilygeroudis, Bernardo Fichera, Farshad Khadivar, Ilaria Lauzana, Kunpeng Yao

We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. We provide two setups that try to highlight the following challenges: (a) manipulating objects via a tool, (b) placing irregularly shaped objects in the correct groove, (c) handling semi-deformable objects, and (d) bimanual coordination. We provide CAD drawings of the task pieces that can be easily 3D printed to ensure ease of reproduction,and detailed description of tasks and protocol for successful reproduction, as well as meaningful metrics for comparison. We propose four categories of submission in an attempt to make the benchmark accessible to a wide range of related fields spanning from adaptive control, motion planning to learning the tasks through trial-and-error learning.

2020

Advancing the Adaptability of Compliant Robot Controllers for Exploration, Interaction, and Manipulation

Farshad Khadivar

EPFL2022