75 research outputs found
A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping
Soft robotic grippers are shown to be high effective for grasping
unstructured objects with simple sensing and control strategies. However, they
are still limited by their speed, sensing capabilities and actuation mechanism.
Hence, their usage have been restricted in highly dynamic grasping tasks. This
paper presents a soft robotic gripper with tunable bistable properties for
sensor-less dynamic grasping. The bistable mechanism allows us to store
arbitrarily large strain energy in the soft system which is then released upon
contact. The mechanism also provides flexibility on the type of actuation
mechanism as the grasping and sensing phase is completely passive. Theoretical
background behind the mechanism is presented with finite element analysis to
provide insights into design parameters. Finally, we experimentally demonstrate
sensor-less dynamic grasping of an unknown object within 0.02 seconds,
including the time to sense and actuate
Plant-inspired behavior-based controller to enable reaching in redundant continuum robot arms
Enabling reaching capabilities in highly redundant continuum robot arms is an active area of research. Existing solutions comprise of task-space controllers, whose proper functioning is still limited to laboratory environments. In contrast, this work proposes a novel plant-inspired behaviour-based controller that exploits information obtained from proximity sensing embedded near the end-effector to move towards a desired spatial target. The controller is tested on a 9-DoF modular cable-driven continuum arm for reaching multiple set-points in space. The results are promising for the deployability of these systems into unstructured environments
To Enabling Plant-like Movement Capabilities in Continuum Arms
Enabling reaching capabilities in highly redundant
continuum soft arms is an active area of research. So far,
it has been heavily addressed through the brain-inspired
notion of internal models, where sensory-motor spaces
are correlated through learning-based computational
frameworks. However, this work investigates an innovative
source of bio-inspiration, i.e., plants, which can interestingly
move towards a desired external stimulus despite the
lack of a central nervous system, thereby, opening avenues
to the development of a new generation of distributed
control strategies for continuum arms. In particular,
reaching is achieved through a combination of distributed
sensing and curvature regulation. This work is a first
translation of moving-by-growing mechanisms in plants
intended to endow continuum and soft robotic arms with
a novel repertoire of motions that can be exploited to
efficiently navigate highly unstructured environments
Pushing with Soft Robotic Arms via Deep Reinforcement Learning
Soft robots can adaptively interact with unstructured environments. However,
nonlinear soft material properties challenge modeling and control. Learningbased
controllers that leverage efficient mechanical models are promising for
solving complex interaction tasks. This article develops a closed-loop pose/force
controller for a dexterous soft manipulator enabling dynamic pushing tasks using
deep reinforcement learning. Force tests investigate the mechanical properties of
a soft robot module, resulting in orthogonal forces of 9  13 N. Then, the policy is
trained in simulation leveraging a dynamic Cosserat rod model of the soft robot.
Domain randomization mitigate the sim-to-real gap while careful reward engineering
induced pose and force control even without explicit force inputs.
Despite the approximate simulation, the sim-to-real transfer achieved an average
reaching distance of 34  14mm (8.1%L  3.4%L), an average orientation error
of 0.40  0.29 rad (23°  17°) and applied pushing forces up to 3 N. Such performance
is reasonable for the intended assistive tasks of the manipulator. The
experiments uncovered that the soft robot interacting with the environment
exhibited torsional and counter-balancing movements. Although not explicitly
enforced, they emerged from the mechanical intelligence of the manipulator.
The results demonstrate the potential of soft robotic manipulation via reinforcement
learning
Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges
In the last few decades, soft robotics technologies have challenged conventional approaches by introducing new, compliant bodies to the world of rigid robots. These technologies and systems may enable a wide range of applications, including human-robot interaction and dealing with complex environments. Soft bodies can adapt their shape to contact surfaces, distribute stress over a larger area, and increase the contact surface area, thus reducing impact forces
The iCub multisensor datasets for robot and computer vision applications
This document presents novel datasets, constructed by employing the iCub
robot equipped with an additional depth sensor and color camera. We used the
robot to acquire color and depth information for 210 objects in different
acquisition scenarios. At this end, the results were large scale datasets for
robot and computer vision applications: object representation, object
recognition and classification, and action recognition.Comment: 6 pages, 6 figure
Robust fractional-order control using a decoupled pitch and roll actuation strategy for the I-support soft robot
This article belongs to the Special Issue Applications of Mathematical Models in Engineering.Tip control is a current open issue in soft robotics; therefore, it has received a good amount of attention in recent years. The desirable soft characteristics of these robots turn a well-solved problem in classic robotics, like the end-effector kinematics and dynamics, into a challenging problem. The high redundancy condition of these robots hinders classical solutions, resulting in controllers with very high computational costs. In this paper, a simplification is proposed in the actuation setup of the I-Support soft robot, allowing the use of simple strategies for tip inclination control. In order to verify the proposed approach, inclination step input and trajectory-tracking experiments were performed on a single module of the I-Support robot, resulting in zero output error in all cases, including those where the system was exposed to disturbances. The comparative results of the proposed controllers, a proportional integral derivative (PID) and a fractional order robust (FOPI) controller, validate the feasibility of the proposed approach, showing a clear advantage in the use of the fractional robust controller for the tip inclination control of the I-Support robot compared to the integer order controller.The research leading to these results has received funding from the project Desarrollo de articulaciones blandas para aplicaciones robóticas, with reference IND2020/IND-1739, funded by the Comunidad Autónoma de Madrid (CAM) (Department of Education and Research), from HUMASOFT project, with reference DPI2016-75330-P, funded by the Spanish Ministry of Economy and Competitiveness, and from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, FaseIV; S2018/NMT-4331), funded by "Programas de Actividades I+D en la Comunidad de Madrid" and cofunded by Structural Funds of the EU. This work was also funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 863212 (PROBOSCIS) and No. 824074 (GROWBOT)
Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task:
Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed in these cycles come at a cost. Such costs force the agent to evaluate a tradeoff between the optimality of the decision making and the time and computational effort required to make it. Several cognitive mechanisms that play critical roles in managing this tradeoff have been identified. These mechanisms include adaptation, learning, memory, attention, and planning. One of the often overlooked outcomes of these cognitive mechanisms, in spite of the critical effect that they may have on the perception-action cycle of organisms, is "emotion." In this study, we hold that emotion can be considered as an emergent phenomenon of a plausible neurocomputational energy regulation mechanism, which generates an internal reward signal to minimize the neural energy consumption of a sequence of actions (decisions), where each action triggers a visual memory recall process. To realize an optimal action selection over a sequence of actions in a visual recalling task, we adopted a model-free reinforcement learning framework, in which the reward signal—that is, the cost—was based on the iteration steps of the convergence state of an associative memory network. The proposed mechanism has been implemented in simulation and on a robotic platform: the iCub humanoid robot. The results show that the computational energy regulation mechanism enables the agent to modulate its behavior to minimize the required neurocomputational energy in performing the visual recalling task
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