Contact-awareness poses a significant challenge in the locomotion control of soft snake robots. This article is to develop bioinspired contact-aware locomotion controllers, grounded in a novel theory pertaining to the feedback mechanism of the Matsuoka oscillator. This mechanism enables the Matsuoka central pattern generator (CPG) system to function analogously to a “spinal cord” in the entire contact-aware control framework. Specifically, it concurrently integrates stimuli, such as tonic input signals originating from the “brain” (a goal-tracking locomotion controller) and sensory feedback signals from the “reflex arc” (the contact reactive controller), for generating different types of rhythmic signals to orchestrate the movement of the soft snake robot traversing through densely populated obstacles and even narrow aisles. Within the “reflex arc” design, we have designed two distinct types of contact reactive controllers: 1) a reinforcement learning-based sensor regulator that learns to modulate the sensory feedback inputs of the CPG system, and 2) a local reflexive controller that establishes a direct connection between sensor readings and the CPG's feedback inputs, adhering to a specific topological configuration. These two reactive controllers, when combined with the goal-tracking locomotion controller and the Matsuoka CPG system, facilitate the implementation of two contact-aware locomotion control schemes. Both control schemes have been rigorous tested and evaluated in both simulated and real-world soft snake robots, demonstrating commendable performance in contact-aware locomotion tasks. These experimental outcomes further validate the benefits of the modified Matsuoka CPG system, augmented by a novel sensory feedback mechanism, for the design of bioinspired robot controllers.

This paper presents a free-swimming, tetherless, cable-driven modular soft robotic fish. The body comprises a series of 3D-printed wave spring structures that create a flexible biologically inspired shape that is capable of an anguilliform swimming gait. A three-module soft robotic fish was designed, fabricated, and evaluated. The motion of the robot was characterized and different combinations of actuation amplitude, frequency, and phase shift were determined experimentally to determine the optimal parameters that maximized speed and minimized the cost of transport (COT). The maximum speed recorded was 0.20 BL/s (body lengths per second) with a COT of 15.82. These results were compared against other robotic and biological fish. We operated the robot, untethered, in a variety of environments to test how it was able to function outside of laboratory settings.

This paper introduces DRAGON: Deformable Robot for Agile Guided Observation and Navigation, a free-swimming deformable impeller-powered vectored underwater vehicle (VUV). A 3D-printed wave spring structure directs the water drawn through the center of the robot by an impeller, enabling it to move smoothly in different directions. The robot is designed to have a narrow cylindrical profile to lower drag and improve agility. It has a maximum recorded speed of 2.1 BL/s (body lengths per second) and a minimum cost of transport (COT) of 2.9. The robot has two degrees of freedom (DoFs) and is capable of performing a variety of maneuvers including a full circle with a radius of 0.23 m (1.4 BL) and a figure eight, which it completed in 4.98 s (72.3 °/s) and 10.74 s respectively. We operated the robot, untethered, in various environments to test the robustness of the design and analyze its motion and performance.

In this paper, we propose an adaptive eye-to-hand vision-based control methodology, which enables a closed-loop grow-to-shape capability for variable length continuum manipulators in 2D. Our method utilizes shape features of the continuum robot, i.e. module curvature and length, which are obtained from the image. Our adaptive control algorithm servos the robot to converge and track the desired values of these features in the image space without the need of a robot model. As a result the robot starts from a minimum length configuration and grows into a given desired shape, always staying on the course of the desired shape. We believe that this approach unlocks capabilities for variable length continuum robots by leveraging their actuation redundancy and avoiding obstacles while carrying out object manipulation or inspection tasks in cluttered and constrained environments. We perform experiments in simulations and on a real robot to assess the performance of our visual servoing algorithm. Our experimental results demonstrate the controllers ability to accurately converge the current features to their references, for a variety of desired shapes in the image, while ensuring a smooth tracking response. We also present some proof of concept results demonstrating the effectiveness of this technique for controlling the robot in constrained environments. Markedly, this is the first successful demonstration for automatic grow-to-shape control using visual feedback for variable length continuum manipulators.

This work describes the development, demonstration, and performance evaluation study of a wearable human machine interface for robotic teleoperation. We present a novel tetherless human machine interface in the form of a backpack, wearable 3D arm motion capture sensors, finger flexion sensors, and pneumatic haptic feedback muscles. The system is integrated in a complete teleoperation framework, enabling users to be immersed in a remote environment through virtual reality headgear, facilitating intuitive manipulation of an industrial articulated arm. The human machine interface samples the kinematic configuration of the user’s arm, hand, and fingers using multiple inertial measurement units and capacitive sensors respectively, and streams it to the teleoperation software stack. The gripping forces experienced at the robot’s end-effector are acquired using a custom three-dimensional Hall-effect magnetic sensor. The system simultaneously renders the kinesthetic and tactile feedback on the user’s fingers through custom designed pneumatically actuated soft robotic haptic muscles. The efficacy of the human machine interface and the teleoperation system was tested and evaluated by conducting user studies, which showed 31.4% faster teleoperation compared to a keypad controller, and 60% less gripping force utilized with haptics enabled. The findings of the study guided the design and prototype development of a printed electronics based stretchable sleeve and glove motion capture unit to improve the portability, ergonomics, and user experience of the human machine interface.

In this paper, we present a novel clothoid-based visual servoing method for controlling the shape of a variable length continuum manipulator. Clothoids are curves with linearly changing curvature. They allow us to obtain a smooth representation of a continuum manipulator's shape in a compact form with few parameters. Using this curve model, we generate image features that are used in an adaptive visual servoing method to drive the robot to a desired shape. The adaptive algorithm estimates and updates a local interaction matrix that maps the rate of change in clothoid features to actuator velocities of the continuum manipulator. As such, the method does not require any robot model or even actuator encoder measurements and only uses the visual clothoid features to control the robot shape. A unique advantage of using our clothoid representation is being able to generate reference shape curves without the need for taking images of the robot at the desired shapes. Experiments demonstrate successful shape and end effector pose convergence for a diverse set of references. Our repeatability tests demonstrate that the system performance is consistent. Notably, we also present the first results in the literature for the vision-based shape control of a variable length continuum robot, extending and contracting to achieve the desired shape.

Robotic manipulation tasks rely on a plethora of environmental and payload information. One critical piece of information for accurate manipulation is the center of mass (CoM) of the object, which is essential for estimating the dynamic response of the system and determining the payload placement. Traditionally, the CoM of a payload is provided prior to manipulation. In order to create a more robust and comprehensive system, this information should be collected by the robotic agent before or during the task run time. This paper presents a method for approximating the CoM of a planar object using a small-scale mobile robot to inform manipulation tasks. On average, our system is able to converge on a CoM estimate in under 30 seconds in simulation and 20 seconds in experiment, with a relative error of 4.95% and 5.46%, respectively.

Intelligent control of soft robots is challenging due to the nonlinear and difficult-to-model dynamics. One promising model-free approach for soft robot control is reinforcement learning (RL). However, model-free RL methods tend to be computationally expensive and data-inefficient and may not yield natural and smooth locomotion patterns for soft robots. In this work, we develop a bioinspired design of a learning-based goal-tracking controller for a soft snake robot. The controller is composed of two modules: An RL module for learning goal-tracking behaviors given the unmodeled and stochastic dynamics of the robot, and a central pattern generator (CPG) with the Matsuoka oscillators for generating stable and diverse locomotion patterns. We theoretically investigate the maneuverability of Matsuoka CPG's oscillation bias, frequency, and amplitude for steering control, velocity control, and sim-to-real adaptation of the soft snake robot. Based on this analysis, we proposed a composition of RL and CPG modules such that the RL module regulates the tonic inputs to the CPG system given state feedback from the robot, and the output of the CPG module is then transformed into pressure inputs to pneumatic actuators of the soft snake robot. This design allows the RL agent to naturally learn to entrain the desired locomotion patterns determined by the CPG maneuverability. We validated the optimality and robustness of the control design in both simulation and real experiments, and performed extensive comparisons with state-of-art RL methods to demonstrate the benefit of our bioinspired control design.

Soft robots are theoretically well-suited to rescue and exploration applications where their flexibility allows for the traversal of highly cluttered environments. However, most existing mobile soft robots are not fast or powerful enough to effectively traverse three dimensional environments. In this paper, we introduce a new mobile robot with a continuously deformable slender body structure, the SalamanderBot, which combines the flexibility and maneuverability of soft robots, with the speed and power of traditional mobile robots. It consists of a cable-driven bellows-like origami module based on the Yoshimura crease pattern mounted between sets of powered wheels. The origami structure allows the body to deform as necessary to adapt to complex environments and terrains, while the wheels allow the robot to reach speeds of up to 303.1 mm/s (2.05 body-length/s). Salamanderbot can climb up to 60-degree slopes and perform sharp turns with a minimum turning radius of 79.9 mm (0.54 body-length).

In this paper, we present a new locomotion control method for soft robot snakes. Inspired by biological snakes, our control architecture is composed of two key modules: A reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns. The two modules are interconnected into a closed-loop system: The RL module, analogizing the locomotion region located in the midbrain of vertebrate animals, regulates the input to the CPG system given state feedback from the robot. The output of the CPG system is then translated into pressure inputs to pneumatic actuators of the soft snake robot. Based on the fact that the oscillation frequency and wave amplitude of the Matsuoka oscillator can be independently controlled under different time scales, we further adapt the option-critic framework to improve the learning performance measured by optimality and data efficiency. The performance of the proposed controller is experimentally validated with both simulated and real soft snake robots.

The human hand serves as an inspiration for robotic grippers. However, the dimensions of the human hand evolved under a different set of constraints and requirements than that of robots today. This paper discusses a method of kinematically optimizing the design of an anthropomorphic robotic hand. We focus on maximizing the workspace intersection of the thumb and the other fingers as well as maximizing the size of the largest graspable object. We perform this optimization and use the resulting dimensions to construct a flexible, underactuated 3D printed prototype. We verify the results of the optimization through experimentation, demonstrating that the optimized hand is capable of grasping objects ranging from less than 1 mm to 12.8 cm in diameter with a high degree of reliability. The hand is lightweight and inexpensive, weighing 333 g and costing less than 175 USD, and strong enough to lift over 1.1 lb (500 g). We demonstrate that the optimized hand outperforms an open-source 3D printed anthropomorphic hand on multiple tasks. Finally, we demonstrate the performance of our hand by employing a classification-based user intent decision system which predicts the grasp type using real-time electromyographic (EMG) activity patterns.

In this work we present a framework that is capable of accurately representing soft robotic actuators in a multiphysics environment in real-time. We propose a constraint-based dynamics model of a 1-dimensional pneumatic soft actuator that accounts for internal pressure forces, as well as the effect of actuator latency and damping under inflation and deflation and demonstrate its accuracy a full soft robotic snake with the composition of multiple 1D actuators. We verify our model's accuracy in static deformation and dynamic locomotion open-loop control experiments. To achieve real-time performance we leverage the parallel computation power of GPUs to allow interactive control and feedback.

Manual palpation is commonly used to localize tumors and other features buried deep inside organs during open surgery. This approach is not feasible in minimally invasive or robotic surgery, as the contact with the tissue is mediated by instruments. To address this problem, we propose a soft robotic skin (SRS) that can be deployed from a small incision and create a stiffness map in a single step. Such a skin is composed of a matrix of soft robotic tactile elements (SRTEs), each one able to expand and record the tissue response during expansion. In this paper, we firstly prove the feasibility of palpation using a single SRTE. Then, we present and test a soft-suction based anchoring mechanism able to keep the SRS in the desired position in contact with the tissue, allowing surgeons to palpate different sides of the organ. Finally, we detail a calibration method for the SRTE, and assess the feasibility of identifying lumps buried inside a soft tissue phantom, and then inside a chicken liver during an ex-vivo trial. Experimental results show that the SRTE was able to differentiate simulated lumps (up to 3.25 mm deep) from healthy tissue in both the phantom and the ex-vivo trials. These results, added to the ability of the suction gripper to compensate for the expansion forces of each SRTE, are paving the way for soft robotic autonomous tools that can be used for intraoperative mapping of tissue cancers.

This paper presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, low-volume deployment, light weight, and inherently safe human interaction and collaboration. Each module has a mass of around 110 g and integrates the actuation, sensing, and control sub-systems necessary for operation. The origami structure has 7.311 Nm/rad (0.128 Nm/degree) torsional stiffness while being capable of bending in two directions and changing arclength down to a fully collapsed state. A maximum contraction of 35 mm and bending angle of 35.5 degrees were achieved with 45 mm arc length. The module is capable of passively supporting a 1-kg mass at its tip, or 4 additional serially connected modules, bending approximately 6 degrees in the worst case. We also show that we can actively compensate for external moments by pre-compressing or pre-bending the module. We utilize an inverse kinematic control scheme and use it for both open and closed loop control following a circular trajectory. Our results indicate that the module motion follows the desired trajectory with an RMS error of 0.681 mm in the horizontal (x-y) plane and 0.373 mm in the z-axis with closed-loop control. We also assembled two origami modules in series and drove them independently, demonstrating the proof-of-concept of a modular origami continuum manipulator.

Soft robotics carries the promise of making robots as capable and adaptable as biological creatures, but this will not be possible without the ability to perform self-sensing and control with precision and repeatability. In this paper, we seek to address this need with the development of a new pneumatically-actuated soft bending actuation module with integrated curvature sensing. We designed and fabricated two different versions of this module: One with a commercially available resistive flex sensor and the other with a magnetic curvature sensor of our own design, and used an external motion capture system to calibrate and validate these two approaches. In addition, we used an iterative sliding mode controller to drive the modules through step curvature references to demonstrate the controllability of the modules as well as compare the usability of the two sensors. We found that the magnetic sensor returned noisy but accurate data, while the flex sensor had minor inaccuracies and it was subject to overshoot but did not exhibit notable noise. Experimental results show that this phenomenon of overshoot from the flex sensor causes active feedback control of the bending actuator to exhibit significant positioning errors. This work demonstrates that our soft bending actuator can be controlled with repeatability and precision, and that our magnetic curvature sensor represents an improvement for use in proprioception and closed-loop control of soft robotic devices.

Soft pneumatic actuators can allow robotic manipulators to interact safely in complex environments in close proximity to humans, but work still needs to be done controlling them more effectively. We explore this area by introducing a 2-degree of freedom (DoF) universal joint module actuated by three reverse Pneumatic Artificial Muscles (rPAMs) and an associated geometric Jacobian-enhanced iterative sliding mode controller. After demonstrating the effectiveness of this controller, we combine two of these modules to form a 4-DoF soft actuated manipulator. To control this modular manipulation system, we propose two controllers: a direct inverse kinematic (IK) controller and an end-effector geometric Jacobian controller. Though both controllers were validated to function effectively, the Jacobian controller was more precise (especially under payload) while the IK controller was more accurate.

Soft pneumatic actuators provide many exciting properties, but controlling them without the use of bulky and expensive flow-control valves can be difficult and computationally expensive. We seek a solution to this problem by introducing an inexpensive and reliable muscle-like linear soft actuator used antagonistically to operate a rigid 1-DoF joint, resulting in a system that combines the advantages of rigid and soft robotics. Using this setup, we performed precise motion control using a sliding mode feedback controller as well as a sliding mode controller augmented by a feedforward term to modulate the state of solenoid valves that drive each actuator. We found that both controllers performed equivalently well in following a step function and in responding to a disturbance. The feedforward augmented controller performed significantly better when following dynamic trajectories over a range of frequencies and with the addition of an external force. The next step will be to modify our valve control scheme to allow for the determination of both the position and stiffness of the joint, better leveraging the advantages of soft pneumatic actuators.

This paper introduces a unique hexapedal locomotion platform tagged TriBot as potential agents for swarm robotic systems. We investigate the differences in performance and kinematic characteristics of two identical prototypes of this mobile robot manufactured using different design and fabrication methods and materials. Among alternatives for mass production, we focus on cut-and-assemble (CA) acrylic bodies and cut-and-fold (CF) origami-inspired polyester structures as two promising methods for manufacturing these agents. Through a comprehensive comparison between the two prototypes, advantages and disadvantages of each robot development approach are presented. This information will enable the selection of the most appropriate robotic platform according to environmental and operational task specifications.

This study demonstrates a new approach to autonomous folding for the body of a 3D robot from a 2D sheet, using heat. We approach this challenge by folding a 0.27-mm sheetlike material into a structure. We utilize the thermal deformation of a contractive sheet sandwiched by rigid structural layers. During this baking process, the heat applied on the entire sheet induces contraction of the contracting layer and thus forms an instructed bend in the sheet. To attain the targeted folding angles, the V-fold spans method is used. The targeted angle θout can be kinematically encoded into crease geometry. The realization of this angle in the folded structure can be approximately controlled by a contraction angle θin. The process is non-reversible, is reliable, and is relatively fast. Our method can be applied simultaneously to all the folds in multi-crease origami structures. We demonstrate the use of this method to create a lightweight mobile robot.

This paper presents the development of a lightweight origami-inspired foldable hexapod robot. Using a single sheet of polyester and a laser cutter, the hexapod robot can be fabricated and assembled in less than one hour from scratch. No screw or other external tools are required for assembly. The robot has built-in polyester fasteners considered in its crease pattern. The design uses four-bar mechanisms, which makes the robot flexible to be adjusted for different speeds or other task metrics. For a given desired locomotion velocity, various parameters of the four-bar mechanisms in the crease pattern can be modified accordingly. Design flexibility, ease of fabrication, and low cost make the robot suitable as an agent for swarm objectives. This work presents the foldable hexapod design and its kinematic analysis. The robot is fabricated, assembled, and tested for functionality. Experimental results show that the robot prototype runs with a maximum forward speed of 5 body lengths per second and turns in place with a speed of 1 revolution per second. The final robot weighs 42 grams.

In this paper we describe the design, fabrication, control, and experimental validation of a soft and highly compliant 2D manipulator. The arm consists of several body segments actuated using bi-directional fluidic elastomer actuators and is fabricated using a novel composite molding process. We use a cascaded PI and PID computation and novel fluidic drive cylinders to provide closed-loop control of curvature for each soft and highly compliant body segment. Furthermore, we develop algorithms to compute the arm's forward and inverse kinematics in a manner consistent with piece-wise constant curvature continuum manipulators. These computation and control systems enable this highly compliant robot to autonomously follow trajectories. Experimental results with a robot consisting of six segments show that controlled movement of a soft and highly compliant manipulator is feasible.

Printing and folding are fast and inexpensive methods for prototyping complex machines. Self-assembly of the folding step would expand the possibilities of this method to include applications where external manipulation is costly, such as micro-assembly, mass production, and space applications. This paper presents a method for self-folding of printed robots from two-dimensional materials based on shape memory polymers actuated by joule heating using embedded circuits. This method was shown to be capable of sequential folding, angle-controlled folds, slot-and-tab assembly, and mountain and valley folds. An inchworm robot was designed to demonstrate the merits of this technique. Upon the application of sufficient current, the robot was able to fold into its functional form with fold angle deviations within six degrees. This printed robot demonstrated locomotion at a speed of two millimeters per second.

In this study, we demonstrate a new approach to autonomous folding for the body of a 3D robot from a 2D sheet using heat. We approach this challenge by folding a 0.27 mm sheet-like material into a structure. We utilize the thermal deformation of a contractive sheet sandwiched by rigid structural layers. During this “baking” process, the heat applied on the entire sheet induces contraction of the contracting layer and, thus, forms an instructed bend in the sheet. To attain the targeted folding angles, the V-fold Spans method is used. The targeted angle θoutcan be kinematically encoded into crease geometry. The realization of this angle in the folded structure can be approximately controlled by a contraction angle θin. The process is non-reversible, is reliable, and it is relatively fast. Our method can be applied simultaneously to all the folds in multi-creased origami structures. We demonstrate the use of this method to create a light-weight mobile robot.

This paper presents the design, fabrication and operation of a hexapod fabricated using a combination of printing and folding flat sheets of polyester. The polyester sheets are cut and engraved with crease patterns, which are then manually folded to create 3D functional modules, inspired by the Japanese art of Origami. These modules, when connected, form a hexapod with two degrees of freedom per leg. All custom mechanical parts are manufactured in a planar fashion using a laser cutter. We created this print-and-fold hexapod as a miniature version of a commercially available platform, to which we compare several metrics, such as weight, walking speed, and cost of transportation. Our print-and-fold hexapod has a mass of 195 g, can walk at speeds of up to 38.1 cm/sec (two body lengths per second), and can be manufactured and assembled from scratch by a single person in approximately seven hours. Experimental results of gait control and trajectory tracking are provided.

This work presents a technique which allows the application of 2-D fabrication methods to build 3-D robotic systems. The ability to print robots introduces a fast and low-cost fabrication method to modern, real-world robotic applications. To this end, we employ laser-engraved origami patterns to build a new class of robotic systems for mobility and manipulation. Origami is suitable for printable robotics as it uses only a flat sheet as the base structure for building complicated functional shapes, which can be utilized as robot bodies. An arbitrarily complex folding pattern can be used to yield an array of functionalities, in the form of actuated hinges or active spring elements. For actuation, we use compact NiTi coil actuators placed on the body to move parts of the structure on-demand. We demonstrate, as a proof-of-concept case study, the end-to-end fabrication and assembly of a simple mobile robot that can undergo worm-like peristaltic locomotion.

This paper presents the design, fabrication, and evaluation of a novel type of valve that uses an electropermanent magnet [1]. This valve is then used to build actuators for a soft robot. The developed EPM valves require only a brief (5 ms) pulse of current to turn flow on or off for an indefinite period of time. EPMvalves are characterized and demonstrated to be well suited for the control of elastomer fluidic actuators. The valves drive the pressurization and depressurization of fluidic channels within soft actuators. Furthermore, the forward locomotion of a soft, multi-actuator rolling robot is driven by EPM valves. The small size and energy-efficiency of EPM valves may make them valuable in soft mobile robot applications.

This paper presents a soft robotic platform that exhibits peristaltic locomotion. The design principle is based on the unique antagonistic arrangement of radial/circular and longitudinal muscle groups of Oligochaeta. Sequential antagonistic motion is achieved in a flexible braided mesh-tube structure with NiTi coil actuators. A numerical model for the mesh structure describes how peristaltic motion induces robust locomotion and details the deformation by the contraction of NiTi actuators. Several peristaltic locomotion modes are modeled, tested, and compared on the basis of locomotion speed. The entire mechanical structure is made of flexible mesh materials and can withstand significant external impacts during locomotion. This approach can enable a completely soft robotic platform by employing a flexible control unit and energy sources.

Atomic force microscope (AFM) based nanomanipulation systems are generally slow, not repeatable and imprecise due to a lack of control on the success of limited attempts in the literature. To improve the amount of control, reliability and precision of such systems, this work proposes an automated nanomanipulation method. Spherical gold nanoparticles with 100 nm diameter are positioned mechanically on a flat mica substrate by contact manipulation by the AFM probe tip to a desired position autonomously. The most significant issue of the manipulation operation is the lack of real-time visual feedback. This issue is solved by developing a robust algorithm for particle center detection and using the AFM cantilever deflection (force) signals to detect contact losses in real-time and to repeat the manipulation again until the target location is reached. Using these solutions, an automated AFM manipulation system is developed and a statistical study is made, where gold nanoparticles are positioned for 50 times to random target positions in different directions and pushing distances. 86% of all the particles could be successfully positioned to the target positions with an accuracy less than 100 nm. Unsuccessful positioning operations are due to the particle sticking to either the tip (8%) or the substrate (6%). Additionally, performance of the successful manipulations are investigated on 60 manipulation operations in 12 different directions and 5 different distances. The metrics used to quantify performance are the final particle position error and the average manipulation speed.

In this paper, a strategy for controlled pushing is presented for microassembly of 4.5 mum polystyrene particles on a flat glass substrate using an atomic force microscope probe tip. Real-time vision based feedback from a CCD camera mounted to a high resolution optical microscope is used to track particle positions relative to the tip and target position. Tip-particle system is modeled in 2D as a nonholonomic differential drive robot. Effectiveness of the controller is demonstrated through experiments performed using a single goal position as well as linking a series of target positions to form a single complex trajectory. Cell decomposition and wavefront expansion algorithms are implemented to autonomously locate a navigable path to a specified target position. Control strategy alleviates problem of slipping and spinning during pushing.

In this work, teleoperated nanomanipulation with force feedback is demonstrated by using an atomic force microscope on the slave side and a haptic device on the master side. Three main topics are addressed that are especially relevant for teleoperation at these scales: transparency, impedance reflection according to human perception, and stability. The proposed passivity based bilateral control scheme provides a stable means of teleoperation with an adaptive force scaling factor according to impedance limits that can be set by the operator and as transparent as possible. As a result, a robust platform to perform teleoperated nanomanipulation on a broad range of materials is achieved. Performance of the resulting bilateral controller is demonstrated in experimental results for simple vertical nanomanipulation touching experiments on glass and polydimethylsiloxane substrates.