2 research outputs found
Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation
Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human–robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.IMOCOe4.0 [EU H2020RIA-101007311]Spanish national funding [PCI2021-121925INTSENSO [MICINN-FEDER-PID2019-
109991GB-I00]INTARE (TED2021-131466B-I00)
projects funded by MCIN/AEI/10.13039/501100011033EU
NextGenerationEU/PRTR to ERThe SPIKEAGE [MICINN629PID2020-113422GAI00]DLROB
[TED2021 131294B-I00]Spanish Ministry
of Science and Innovation MCIN/AEI/10.13039/501100011033
and European Union NextGenerationEU/PRT
Compliant robot control using cerebellar spiking neural networks, a biologically inspired approach
In the last decades, a new robotics paradigm has been introduced due to physical
human-robot interaction (HRI) and the use of collaborative robots (cobots) equipped
with low-power actuators and elastic components. This scenario requires the use of
cobot controllers able to operate in unstructured environments and that do not depend
on the accurate mathematical modeling of the nonlinear dynamics introduced by elastic
elements. Robot behavior in this context is required to emulate the adaptability and
flexibility of human behavior as much as possible.
The cerebellum, pivotal for human motor control, has long been proposed as an
adaptive controller, and its regular neural structure has allowed the development of
computational models which replicate, to some extent, its structural and functional
properties. Here, we propose a cerebellar-based adaptive controller able to provide
torque control of a cobot with nonlinear dynamics. Using spiking neural networks we
replicate the cerebellum neural topology and synaptic plasticity mechanisms. We then
embed the biologically plausible cerebellar network at the core of a cobot control loop.
The spike-processing computational cost of biologically plausible cerebellar models has
prevented their real-world applicability, thus relegating them to mere theoretical or
simulated models. Within this dissertation, we prove the applicability of our
biologically plausible cerebellar controller in real-world control problems. We present a
cerebellar spiking neural network which is large enough to provide the required
resolution for torque control of six degrees of freedom in real-time, and hence can
operate real cobots. The cerebellar controller provides fine accuracy in the execution of
different motor tasks thanks to the deployed cerebellar learning mechanisms. Besides,
the controller is also able to adapt the cobot behavior to unstructured changes directly
affecting the cobot dynamics. Furthermore, the aforementioned cerebellar control
learning mechanisms can also cope with sensorimotor delays affecting the robotcontroller
communication, a well-known source of control loop instability.
Sensorimotor latency is unavoidable in the central nervous system (CNS), however, it
does not jeopardize the stability of motor control thanks to, among others, cerebellar
predictive behavior. We prove the cerebellar controller robust against sensorimotor
delays of different nature, thus applying to robotics another intrinsic feature of the
cerebellum.
In addition to cerebellar control, we expand the biologically inspired approach with
other key elements of the CNS and musculoskeletal system. We present some first
results of adding spinal cord circuits to the cerebellar controller. The spinal cord, using direct muscle feedback to allow fast-stretch reflexes and muscle activity regulation, is
found to improve cerebellar learning and robustness against perturbations. As next step
we will integrate the spinal cord circuits and the cerebellar controller operating the
cobot, for which muscle dynamics will need to be added to the control loop. Here we
present a preliminary approach for the integration of muscle dynamics within the cobot
control loop, which is shown capable of modifying the motion stiffness of the cobot by
changing the cocontraction degree of antagonistic muscle pairs. Different stiffness
profiles would allow the robot behavior to cover different degrees of admittance and
impedance control, of interest to physical HRI as those control modes directly impact
how the robot reacts to external interactions (admittance control performs better in soft
environments, while impedance control favors stiff environments).
For collaborative robotics to succeed, robot performance must emulate the adaptability
and flexibility of human behavior. Hence, the biological substrate behind human
conduct could be used as inspiration to bring robot behavior closer to our inherent
motor capabilities. Human behavior is sustained by both hardware and software: the
biomechanics of the musculoskeletal system together with the control provided by the
CNS allow us to interact with others and the environment. On the hardware side, robot
design is increasingly mimicking the dynamics of living beings. On the software side,
the study and understanding of the different CNS areas and their computational
replication can expand the family of controllers able to provide adaptive, compliant
robot control. Here, we benefit from decades of neuroscience studies about the
cerebellum structure and functioning, and apply those findings to current robotic
challenges.Tesis Univ. Granada