Quick-cast: A method for fast and precise scalable production of fluid-driven elastomeric soft actuators

Fluid-driven elastomeric actuators (FEAs) are among the most popular actuators in the emerging field of soft robotics. Intrinsically compliant, with continuum of motion, large strokes, little friction, and high power-to-weight ratio, they are very similar to biological muscles, and have enabled new applications in automation, architecture, medicine, and human-robot interaction. To foster future applications of FEAs, in this paper we present a new manufacturing method for fast and precise scalable production of complex FEAs of high quality (leak-free, single-body form, with <0.2 mm precision). The method is based on 3d moulding and supports elastomers with a wide range of viscosity, pot life, and Young's modulus. We developed this process for two different settings: one in laboratory conditions for fast prototyping with 3d printed moulds and using multi-component liquid elastomers, and the other process in an industrial setting with 3d moulds micromachined in metal and applying compression moulding. We demonstrate these methods in fabrication of up to several tens of two-axis, three-chambered soft actuators, with two types of chamber walls: cylindrical and corrugated. The actuators are then applied as motion drivers in kinetic photovoltaic building envelopes

Constrained Efficient Global Optimization of Expensive Black-box Functions

We study the problem of constrained efficient global optimization, where both the objective and constraints are expensive black-box functions that can be learned with Gaussian processes. We propose CONFIG (CONstrained efFIcient Global Optimization), a simple and effective algorithm to solve it. Under certain regularity assumptions, we show that our algorithm enjoys the same cumulative regret bound as that in the unconstrained case and similar cumulative constraint violation upper bounds. For commonly used Matern and Squared Exponential kernels, our bounds are sublinear and allow us to derive a convergence rate to the optimal solution of the original constrained problem. In addition, our method naturally provides a scheme to declare infeasibility when the original black-box optimization problem is infeasible. Numerical experiments on sampled instances from the Gaussian process, artificial numerical problems, and a black-box building controller tuning problem all demonstrate the competitive performance of our algorithm. Compared to the other state-of-the-art methods, our algorithm significantly improves the theoretical guarantees, while achieving competitive empirical performance.Comment: Accepted to ICML 202

Bayesian Optimization of Expensive Nested Grey-Box Functions

We consider the problem of optimizing a grey-box objective function, i.e., nested function composed of both black-box and white-box functions. A general formulation for such grey-box problems is given, which covers the existing grey-box optimization formulations as special cases. We then design an optimism-driven algorithm to solve it. Under certain regularity assumptions, our algorithm achieves similar regret bound as that for the standard black-box Bayesian optimization algorithm, up to a constant multiplicative term depending on the Lipschitz constants of the functions considered. We further extend our method to the constrained case and discuss several special cases. For the commonly used kernel functions, the regret bounds allow us to derive a convergence rate to the optimal solution. Experimental results show that our grey-box optimization method empirically improves the speed of finding the global optimal solution significantly, as compared to the standard black-box optimization algorithm

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal-dual contextual Bayesian optimization algorithm is proposed that achieves sublinear cumulative regret with respect to the dynamic optimal solution under certain regularity conditions. Furthermore, the algorithm achieves zero time-average constraint violation, ensuring that the average value of the constraint function satisfies the desired constraint. The method is applied to both sampled instances from Gaussian processes and a continuous stirred tank reactor parameter tuning problem; simulation results show that the method simultaneously provides close-to-optimal performance and maintains constraint feasibility on average. This contrasts current state-of-the-art methods, which either suffer from large cumulative regret or severe constraint violations for the case studies presented

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging simple Rules

Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the system often allows us to design simple rules we expect good policies to follow at all times. In this work, we hence propose a simple yet effective modification of continuous actor-critic frameworks to incorporate such rules and avoid regions of the state-action space that are known to be suboptimal, thereby significantly accelerating the convergence of RL agents. Concretely, we saturate the actions chosen by the agent if they do not comply with our intuition and, critically, modify the gradient update step of the policy to ensure the learning process is not affected by the saturation step. On a room temperature control case study, it allows agents to converge to well-performing policies up to 6-7x faster than classical agents without computational overhead and while retaining good final performance.Comment: Submitted to CDC 202

Soft-Robotic-Driven Adaptive Photovoltaic Building Envelopes

Current building envelopes are predominantly static, manually operated, and do not have integrated energy harvesting devices. As opposed to this, kinetic building envelopes with integrated photovoltaics, have the potential to improve building energy performance and occupant comfort by regulating solar heat gains and natural lighting while simultaneously generating electricity on site. If equipped with reflecting elements, the adaptive envelopes can be also used to redistribute solar radiation among neighbouring buildings for generation of thermal energy or mitigation of urban heat-island effects. Even though various approaches to kinetic and climate adaptive envelopes exist, they are limited to theoretical studies, small-scale prototypes, and single, exhibition-type prototypes. The reason for this is in the actuation mechanisms used, which are typically very complex and are comprised of many mechanical components, such as electromotors, rails, pulleys, and cables. This increases the need for maintenance and reduces the lifespan of the envelope. To realize the full potential of adaptive building envelopes, a robust and cost-effective way to reliably move their elements is required. Pneumatically-driven elastomeric actuators, as commonly found in soft robotics, have been identified in this work as a simple, low cost, and robust actuation solution. The toughness, relative chemical inertness, and low glass-transition temperature of silicone elastomers make them ideal for tolerating external environmental conditions, such as rain, cold, heat, and wind. Inherently compliant, they are robust to external disturbances. Pneumatically-powered, they exhibit a high force-to-weight ratio, large strokes, and low mechanical impedance. The challenge is in their design, fabrication, and control. In particular, one of the core challenges of soft actuators is to be able to actively vary their stiffness. The variable stiffness, in this case, would enable actuators to stabilise the envelope panels in wind. In this work, a novel hybrid, soft-hard-material, pneumatically-driven, two-axis actuator with variable stiffness is introduced. It comprises a single body three-chambered elastomeric actuator and a metal two-axis joint from classical mechanics. The metal joint provides structural stability for the soft actuator, thus enabling its more precise and controlled movements, higher forces, and variable stiffness, while all the advantages of pneumatically-driven elastomeric actuators are retained. The corrugated design of chambers’ walls allows fast (0.1 s) actuation and reduction of strain in the material, thus increasing the lifespan of the actuator. The actuator achieved 30,000 cycles in laboratory conditions without breaking; assuming four cycles a day, this is equivalent to a 20-year lifespan. The inherent compliance, pneumatic actuation, and compact design allowed for the reduction of actuator’s size, weight, and complexity compared to an actuator of the same performance that would be constructed in classical mechanics, for example using two electromotors, gearboxes, and spring. As such, this actuator enabled construction of modular, lightweight adaptive building envelopes with integrated energy harvesting devices, such as thin film photovoltaic panels. The soft actuators were produced using a novel method for fast and precise manufacturing of elastomeric actuators, called Quick-cast, introduced also in this work. A novel distributed, modular pneumatic control system is also introduced in this work. It allows pressure to be locally controlled at each envelope module by receiving control signals from a central controlling unit, while the measurements from the orientation sensor, an inertial measurement unit, are sent to the central unit. Such a pneumatic orientation control system enables expansion of the envelope to a wide range of shape and sizes, with only a few steps required to connect a new envelope module with the soft actuator. Several envelope prototypes have been constructed and tested in real weather conditions in solar tracking experiments. Electricity gains of 30-50% have been measured for a South oriented envelope with 16 widely distributed panels; these gains correspond well with theoretical estimates. The gain in solar tracking depends largely on envelope orientation, climatic region, and the distribution of the elements within the envelope. However, the total building energy savings also include the effects of active shading. Estimated energy savings for an office in Zurich with such an adaptive photovoltaic envelope are 20-80% more than an equivalent static system, depending on the building type. We estimated the self-energy consumption, that is, the energy used for pneumatic control vs. the total energy produced, to be about 3%, based on measurements

Control Of Compliant Anthropomimetic Robot Joint

In this paper we propose a control strategy for a robot joint which fully mimics the typical human joint structure. The joint drive is based on two actuators (dc motors), agonist and antagonist, acting through compliant tendons and forming a nonlinear multi-input multi-output (MIMO) system. At any time, we consider one actuator, the puller, as being responsible for motion control, while the role of the other is to keep its tendon force at some appropriate low level. This human-like and energetically efficient approach requires the control of "switching", or exchanging roles between actuators. Moreover, an algorithm based on adaptive force reference is used to solve a problem of slacken tendons during the switching and to increase the energy efficiency. This approach was developed and evaluated on increasingly complex robot joint configurations, starting with linear and noncompliant system, and finishing with nonlinear and compliant system

The Puller-Follower Control of Compliant and Noncompliant Antagonistic Tendon Drives in Robotic Systems

This paper proposes a new control strategy for noncompliant and compliant antagonistic tendon drives. It is applied to a succession of increasingly complex single&#8208;joint systems, starting with a linear and noncompliant system and ending with a revolute, nonlinearly tendon coupled and compliant system. The last configuration mimics the typical human joint structure, used as a model for certain joints of the anthropomimetic robot ECCEROBOT. The control strategy is based on a biologically inspired puller&#8208; follower concept, which distinguishes the roles of the agonist and antagonist motors. One actuator, the puller, is considered as being primarily responsible for the motion, while the follower prevents its tendon from becoming slack by maintaining its tendon force at some non&#8208;zero level. Certain movements require switching actuator roles; adaptive co&#8208;contraction is used to prevent tendons slackening, while maintaining energetic efficiency. The single&#8208;joint control strategy is then evaluated in a multi&#8208; joint system. Dealing with the gravitational and dynamic effects arising from the coupling in a multi&#8208;joint system, a robust control design has to be applied with on&#8208;line gravity compensation. Finally, an experiment corresponding to object grasping is presented to show the controlle

Modelling and control of a compliantly engineered anthropomimetic robot in contact tasks

This paper attempts to develop a dynamic model and design a controller for a fully anthropomorphic, compliantly driven robot. To imitate muscles, the robot's joints are actuated by DC motors antagonistically coupled through tendons. To ensure safe interaction with humans in a humancentered environment, the robot exploits passive mechanical compliance, in the form of elastic springs in the tendons. To enable simulation, the paper first derives a mathematical model of robot dynamics, starting from the "Flier" approach. The control of the antagonistic drives is based on a biologically inspired puller-and-follower concept where at any instant the puller is responsible for the joint motion while the follower keeps the inactive tendon from slackening. In designing the controller, it was necessary to use the advanced theory of nonlinear control for dealing with individual joints, and then to apply the theory of robustness in order to extend control to the multi-jointed robot body