Soft robots undergo large nonlinear spatial deformations due to both inherent actuation and external loading. The physics underlying these deformations is complex, and often requires intricate analytical and numerical models. The complexity of these models may render traditional modelbased control difficult and unsuitable. Model-free methods offer an alternative for analyzing the behavior of such complex systems without the need for elaborate modeling techniques. In this paper, we present a model-free approach for open loop position control of a soft spatial continuum arm, based on deep reinforcement learning. The continuum arm is pneumatically actuated and attains a spatial workspace by a combination of unidirectional bending and bidirectional torsional deformation. We use Deep-Q Learning with experience replay to train the system in simulation. The efficacy and robustness of the control policy obtained from the system is validated both in simulation and on the continuum arm prototype for varying external loading conditions.

There are a number of instances in nature where long and slender objects are grasped by a continuum arm spirally twirling around the object, thereby increasing the area of contact and stability between the gripper and the object. This paper investigates the design and modeling of spiral grippers using pneumatic fiber-reinforced actuators. The paper proposes two reduced order models, a pure helical model, and a spatial Cosserat rod model to capture the deformed behavior of the gripper using the mechanics of fiber-reinforced actuators in the presence of self-weight. While the former model can yield closed form expressions that aid in design, the deformation parameters deviate by greater than 40% of its length for actuators longer than 200 mm. However, the Cosserat rod model deviates by less than 8% of its length for two different prototypes validated in this work. The deformation of the gripper is then correlated to the number of spiral turns achievable about the object, which determines the quality of the grip. Together, they enable a systematic framework where the gripper parameters can be designed for a given range of object sizes to be handled. This framework is experimentally validated by successful gripping of a range of slender objects that lie between a 20 mm diameter tubelight and a 60 mm diameter PVC pipe.

Soft robots undergo large nonlinear spatial deformations due to both inherent actuation and external loading. The physics underlying these deformations is complex, and often requires intricate analytical and numerical models. The complexity of these models may render traditional model based control difficult and unsuitable. Model-free methods offer an alternative for analyzing the behavior of such complex systems without the need for elaborate modeling techniques.In this paper, we present a model-free approach for open loop position control of a soft spatial continuum arm, based on deep reinforcement learning. The continuum arm is pneumatically actuated and attains a spatial workspace by a combination ofunidirectional bending and bidirectional torsional deformation. We use Deep-Q Learning with experience replay to train the system in simulation. The efficacy and robustness of the control policy obtained from the system is validated both in simulation and on the continuum arm prototype for varying external loading conditions

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Soft Continuum Arms (SCAs) have a large workspace, dexterity, and adaptability, but at the cost of complex design construction highlighted by concatenating several serial segments. In this paper, we propose a new design architecture for SCAs composed of a parallel combination of pneumatic actuators. The BR2 SCA featured in this work is asymmetric as it combines one soft bending (B) actuator and two soft rotating (R2) actuators as opposed to state of art symmetric architectures that adopt bending segments. Spatial deformation is obtained by combining the bending and rotating feature of the individual actuators. This paper also formulates an approximate forward analysis method based on Kirchhoff rod equations to predict the spatial deformation under external loads with an accuracy less than 9% of the SCAs length. In addition, the model also takes into account the ‘coupling effect’ inherent to the asymmetric parallel combination, where actuating rotating actuator attenuates the bending performance and vice versa. Consequently, this work also refines the design of the SCA that minimizes the coupling effect. A detailed performance study of the refined BR2 manipulator on a swiveling base demonstrates a larger workspace and higher dexterity when compared with state of art single section SCAs. The performance of the design is validated through different tasks like obstacle avoidance, pick and place task and whole arm grasping. These performance attributes surpass any other single segment soft module and is a potential building block for constructing customized SCAs.

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Soft continuum manipulators have large workspace, dexterity and adaptability, but at the cost of complex design construction highlighted by concatenating several individually controlled serial segments. In this paper, we propose a new designarchitecture for a soft continuum manipulator composed of a parallel combination of pneumatic actuators. The BR2 manipulator, featured in this paper is asymmetric as it combines one soft bending (B) actuator and two soft rotating (R2) actuators, as opposed to state of the art symmetric architectures that adopt bending segments. Spatial deformation modes are achieved by combining curvature and torsion of the individual actuators. This paper formulates a forward analysis method based on Cosserat rod mechanics to predict the spatial deformation of the manipulator under the effect of external loads with an accuracy less than 9 % of the manipulator length. The model takes into account ‘the coupling effect’ inherent to the asymmetric combination, where pressurizing the rotating actuator attenuates the bendingcurvature and vice versa. Consequently, the paper studies the optimal design of the manipulator constituents that minimize the coupling, and thus maximize the workspace and dexterity. A detailed performance study of the BR2 manipulator on aswiveling base demonstrates a spatial workspace quantified by an axisymmetric area, and sufficient dexterity such that at least 87% of the workspace can be approached with two or more orientations. These are validated through obstacle avoidance, and a pick and place task. The manipulator is also capable ofwhole arm manipulation by spiraling along cylindrical objects of varying diameters. These performance attributes surpass any other single segment module and is a potential building block for constructing customized continuum manipulators.