Octopus arms exhibit exceptional flexibility

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kennedy, E. B. L., Buresch, K. C., Boinapally, P., & Hanlon, R. T. Octopus arms exhibit exceptional flexibility. Scientific Reports, 10(1), (2020): 20872. doi:10.1038/s41598-020-77873-7.The octopus arm is often referred to as one of the most flexible limbs in nature, yet this assumption requires detailed inspection given that this has not been measured comprehensively for all portions of each arm. We investigated the diversity of arm deformations in Octopus bimaculoides with a frame-by-frame observational analysis of laboratory video footage in which animals were challenged with different tasks. Diverse movements in these hydrostatic arms are produced by some combination of four basic deformations: bending (orally, aborally; inward, outward), torsion (clockwise, counter-clockwise), elongation, and shortening. More than 16,500 arm deformations were observed in 120 min of video. Results showed that all eight arms were capable of all four types of deformation along their lengths and in all directions. Arms function primarily to bring the sucker-lined oral surface in contact with target surfaces. Bending was the most common deformation observed, although the proximal third of the arms performed relatively less bending and more shortening and elongation as compared with other arm regions. These findings demonstrate the exceptional flexibility of the octopus arm and provide a basis for investigating motor control of the entire arm, which may aid the future development of soft robotics.We gratefully acknowledge funding from Grant N00014-19-1-2445 from the Office of Naval Research, Tom McKenna and Marc Steinberg, Program Managers. We also thank the staff of the Marine Resources Center at MBL for assistance with water quality measurements, seawater system maintenance, and collection of food items for octopuses

Exploiting Multi Stability of Compliant Locking Mechanism for Reconfigurable Articulation in Robotic Arm

This study analyzes a biology inspired approach of utilizing a compliant unit actuator to simplify the control requirements for a soft robotic arm. A robot arm is constructed from a series of compliant unit actuators that precisely actuate between two stable states. The extended state can be characterized as a rigid link with a high bending stiffness. The compressed state can be characterized as a flexible joint with a low bending stiffness. Without the use of an external power source, the bistable mechanism remains in each of the stable states. The unit actuator can demonstrate pseudo-linkage kinematics that require less control parameters than entirely soft manipulators. An advantage of using compliant mechanisms to design a robotic arm is that the bending stiffness ratio between the extended and compressed states is related to the frame and flexural member geometry. Post buckling characteristics of thin flexural members, combined with a cantilever style frame design gives the unit actuator versatile advantages over existing actuator designs like layer jamming and shape memory polymers. To achieve efficient movement with the optimized unit actuator design, experimental validation was performed, and a robotic arm prototype was fabricated. The tendon-driven robotic arm consisted of three modules and proved the capability of transforming and rotating in the eight configurations. The deformations of the robotic arm are accurately predicted by the kinematic model and validate the compliant mechanism arm and simple control system

Macrobend optical sensing for pose measurement in soft robot arms

This paper introduces a pose-sensing system for soft robot arms integrating a set of macrobend stretch sensors. The macrobend sensory design in this study consists of optical fibres and is based on the notion that bending an optical fibre modulates the intensity of the light transmitted through the fibre. This sensing method is capable of measuring bending, elongation and compression in soft continuum robots and is also applicable to wearable sensing technologies, e.g. pose sensing in the wrist joint of a human hand. In our arrangement, applied to a cylindrical soft robot arm, the optical fibres for macrobend sensing originate from the base, extend to the tip of the arm, and then loop back to the base. The connectors that link the fibres to the necessary opto-electronics are all placed at the base of the arm, resulting in a simplified overall design. The ability of this custom macrobend stretch sensor to flexibly adapt its configuration allows preserving the inherent softness and compliance of the robot which it is installed on. The macrobend sensing system is immune to electrical noise and magnetic fields, is safe (because no electricity is needed at the sensing site), and is suitable for modular implementation in multi-link soft continuum robotic arms. The measurable light outputs of the proposed stretch sensor vary due to bend-induced light attenuation (macrobend loss), which is a function of the fibre bend radius as well as the number of repeated turns. The experimental study conducted as part of this research revealed that the chosen bend radius has a far greater impact on the measured light intensity values than the number of turns (if greater than five). Taking into account that the bend radius is the only significantly influencing design parameter, the macrobend stretch sensors were developed to create a practical solution to the pose sensing in soft continuum robot arms. Henceforward, the proposed sensing design was benchmarked against an electromagnetic tracking system (NDI Aurora) for validation

A New Approach to Dynamic Modeling of Continuum Robots

ABSTRACT In this thesis, a new approach for developing practically realizable dynamic models for continuum robots is proposed. Based on the new dynamic models developed, a novel technique for analyzing the capabilities of continuum manipulators to be employed in various real world applications has also been proposed and developed. A section of a continuum arm is modeled using lumped model elements (masses, springs and dampers). It is shown that this model, although an approximation to a continuum structure, can be used to conveniently analyze the dynamics of the arm with suitable tradeoff in accuracy of modeling. This relatively simple model is more plausible to implement in an actual real-time controller when compared to other techniques of modeling continuum arms. Principles of Lagrangian dynamics are used to derive the expressions for the generalized forces in the system. The force exerted by McKibben actuators at different pressure level - length pairs is characterized and is incorporated into this dynamic model. The constraints introduced in the analytical model conform to the physical and operational limitations of the Octarm VI continuum robot manipulator. The model is validated by comparing the results of numerical simulation with the physical measurements of a continuum arm prototype built using McKibben actuators. Based on the new lumped parameter dynamic model developed for continuum robots, a technique for deducing measures of manipulability, forces and impacts that can be sustained or imparted by the tip of a continuum robot has been developed. These measures are represented in the form of ellipsoids whose volume and orientation gives information about the various functional capabilities (end effector velocities, forces and impacts) of the arm at a particular configuration. The above mentioned ellipsoids are exemplified for different configurations of the continuum section arm and their physical significances are analyzed. The new techniques proposed and methodologies adopted in this thesis supported by experimental results represent a significant contribution to the field of continuum robots

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays