California blackworms constitute a recently identified animal system exhibiting unusual collective behaviors, in which dozens to thousands of worms entangle to form a “blob” capable of actions like locomotion as an aggregate. In this paper we describe a system of pneumatic soft robots inspired by the blackworms, intended for the study of collective behaviors enabled and mediated by such physical entanglement. Both the robots and worms have high aspect ratio ($\gtrsim 1: 50$), intertwine in complex 3D configurations, operate both in air and underwater, and can locomote both individually and as a collective. We demonstrate and characterize locomotion for both individual robots and entangled blobs, explore the tunability of entanglement strength, and compare these to the analogous versions in living worms. The robots provide a testbed for studying mechanisms underlying behaviors observed in worm blobs, as well as serving as a platform for studies of novel collective behaviors based on physical entanglement.

“Robot factors” design, analogous to ergonomics for humans, seeks to create devices and equipment that can be readily operated by robots, by considering typical capabilities of current robots throughout the design process. While a number of principles and heuristics for robot factors design have been identified, the successful design of hardware operable by autonomous robots often depends in practice on the designer's intuition about robot capabilities, developed through personal experience working with robots. Here we present a tool we have developed to help evaluate a potential device design for usability by a robot, by allowing a designer to in effect teleoperate a virtual robot and attempt the operation of the device. The tool uses a 3D physics-based simulation built in Unity, and a Phantom Omni / Geomagic Touch haptic device that controls the virtual robot's end-effector and provides force feedback. Through user studies, we show that the use of this tool can significantly improve a user's estimation of the suitability of a design for robot operation, in two case studies involving replacing a unit in a modular hardware system and unzipping a canvas bag. By incorporating the use of such a tool early in the design cycle, designers can more effectively develop equipment to be used by autonomous robots without themselves needing direct robotics experience; as a result, robots will be able to take on more tasks in the nearer term with current robot technology.

In this paper, we discuss the role of gripper compliance in successful grasping and manipulation of thin, flexible materials. We show, both conceptually and empirically, that each axis of compliance in a planar gripper provides unique benefits in this domain. Vertical compliance allows robust grasping of thin materials in the presence of large uncertainty in positioning. Lateral compliance increases opportunity to respond to unexpected snags by increasing the time window over which tensile forces are applied. Rotational compliance avoids damage to objects by decreasing the maximum tensile forces applied during snags. We explore these three benefits through empirical tests comparing a rigid gripper to a soft gripper, evaluating the level of vertical uncertainty each can handle for prehensile and non-prehensile manipulation, as well as the forces and displacements incurred during snags. The results show how a soft gripper's three-axis compliance provides a passive ability to prevent damage to delicate materials.

Robots are increasingly being called on to operate in settings and on tasks originally designed for humans, or where humans are also expected to work. Accordingly, the hardware and tools to be packaged, operated, or maintained are typically designed for use by humans, not robots. Robot autonomy in such cases can be expedited by a “robot factors” approach to the design of hardware, analogous to ergonomics for humans, taking typical current robot capabilities into account during the design process. In this paper, we present two case studies of redesigning mission-critical hardware in space habitats to facilitate autonomous robot operation. In both cases, hardware that previously required dexterous bi-manual manipulation is redesigned such that the entire maintenance task can be completed by a single robotic arm with a standard parallel jaw gripper. We demonstrate successful autonomous replacement of modules in the two hardware systems, and characterize how orientation and compliance of a grasp helps compensate for positioning errors. Based on our findings, we identify several key design strategies that underpin the robot factors approach to designing robot-friendly hardware, including consolidating compound actions into simpler mechanisms, constraining required motions to a single axis, and introducing mechanical compliance to mitigate the effects of pose uncertainties.

We present a decentralized control algorithm for robots to aid in carrying an unknown load. Coordination occurs solely through sensing of the forces on or movement of the shared load. Robots prevent undesired motion of the load while permitting movement in the task-relevant subspace, and stabilize against unexpected events by a transient decrease in compliance. The algorithm requires no direct communication between agents, and minimal knowledge of the system or task. We demonstrate the approach in simulation using a commercially available compliant robotic platform.

Research in autonomous robots for construction has largely focused on ground-based robots whose reach constrains the size of what they can build, or on climbing or aerial robots that build solid or unroofed structures. Autonomous construction of larger, multistory buildings, or bridges spanning unsupported distances, would require robots that build sturdy structures supporting their own weight. In this paper, we present VaultBot, a system of autonomous robots that build a load-bearing spanning vault using identical modular blocks. The custom blocks employ mechanical and other features to facilitate robotic manipulation and locomotion, and can be removed from and replaced in an assembled structure as a way of repairing damage. We characterize the system's performance and failure modes, and demonstrate reliable autonomous assembly for a structure composed of 46 blocks. Blocks can be made collapsible and deployable as a way of reducing mass and volume that must be transported to a construction site. Such a system could be used to help enable construction of protective shelters in challenging environments, such as disaster relief scenarios, arctic settings, or extraterrestrial habitats.

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Soil stabilization is a fundamental component of nearly all construction projects, ranging from commercial construction to environmental restoration projects. Previous work in autonomous construction has generally not considered these essential stabilization and anchoring tasks. In this work we present Romu, an autonomous robot capable of building continuous linear structures by using a vibratory hammer to drive interlocking sheet piles into soil. We report on hardware parameters and their effects on pile driving performance, and demonstrate autonomous operation in both controlled and natural environments. Finally, we present simulations in which a small swarm of robots build with sheet piles in example terrains, or apply an alternate spray-based stabilizing agent, and quantify the ability of each intervention to mitigate hydraulic erosion.

Multi-agent systems demonstrate the ability to collectively perform complex tasks (e.g., construction, search, and locomotion) with greater speed, efficiency, or effectiveness than could a single agent alone. Direct and indirect coordination methods allow agents to collaborate to share information and adapt their activity to fit dynamic situations. A well-studied example is quorum sensing (QS), a mechanism allowing bacterial communities to coordinate and optimize various phenotypes in response to population density. Here we implement, for the first time, bio-inspired QS in robots fabricated from DNA origami, which communicate by transmitting and receiving diffusing signals. The mechanism we describe includes features such as programmable response thresholds and quorum quenching, and is capable of being triggered by proximity of a specific target cell. Nanoscale robots with swarm intelligence could carry out tasks that have been so far unachievable in diverse fields such as industry, manufacturing, and medicine.

Construction automation has historically been driven by top-down implementations of specific tasks, which are neither responsive nor resilient to dynamic situations, and often require centralized control or human supervision. Previous work on robotic assembly has generally neglected to consider forces acting on the structure, whether in the completed structure alone or throughout the building process. In this paper, we investigate the utility of local force measurements in guiding construction by a distributed team of strut-climbing robots, focusing on a scenario involving building an unsupported span out across a gap in a two-dimensional vertical plane, as a step towards building a bridge. We show that such measurements enable robots to build structures that cantilever significantly further than those built by robots without access to such information, while maintainig stability throughout the building sequence. We consider both structures securely anchored to the ground and those resting unanchored atop it, using a counterbalancing approach in the latter case to permit cantilevering. The principles explored in simulation are also demonstrated in hardware, including a prototype strut-climbing robot and truss components, incorporating a cost-effective sensor implementation that reports the requisite force information.

Commercially available depth sensing devices are primarily designed for domains that are either macroscopic, or static. We develop a solution for fast microscale 3D reconstruction, using off-the-shelf components. By the addition of lenses, precise calibration of camera internals and positioning, and development of bespoke software, we turn an infrared depth sensor designed for human-scale motion and object detection into a device with mm-level accuracy capable of recording at up to 30Hz.

There is a widely recognized need for improved STEM education and increased technological literacy. Robots represent a promising educational tool with potentially large impact, due to their broad appeal and wide relevance; however, many existing educational robot platforms have cost as a barrier to widespread use. Here we present AERobot, a simple low-cost robot that can be easily used for introductory programming and robotics teaching, starting from a primary or middle school level. The hardware is open-source and can be built for ~$10 per robot, making it possible for each student to have (and keep) their own robot, while still encompassing a rich sensor suite enabling a variety of activities. A free, open-source graphical programming environment allows students without previous programming experience to command the robot. We report on the results of three sessions of a one-week pilot course held in the summer of 2014 by STEM summer camp i2 Camp.

Roboticists, biologists, and chemists are now producing large populations of simple robots, but controlling large populations of robots with limited capabilities is difficult, due to communication and onboard-computation constraints. Direct human control of large populations seems even more challenging. In this paper we investigate control of mobile robots that move in a 2D workspace using three different system models. We focus on a model that uses broadcast control inputs specified in the global reference frame. In an obstacle-free workspace this system model is uncontrollable because it has only two controllable degrees of freedom - all robots receive the same inputs and move uniformly. We prove that adding a single obstacle can make the system controllable, for any number of robots. We provide a position control algorithm, and demonstrate through extensive testing with human subjects that many manipulation tasks can be reliably completed, even by novice users, under this system model, with performance benefits compared to the alternate models. We compare the sensing, computation, communication, time, and bandwidth costs for all three system models. Results are validated with extensive simulations and hardware experiments using over 100 robots.

Modular robots can potentially assemble into a wide range of configurations to locomote in different environments. However, designing locomotion strategies for each configuration is often tedious and has generally relied on a priori known connection geometry. Here we present a framework for 2D modular robots made of square modules assembled with arbitrary geometry, which achieve collective and directed locomotion with no centralized controller. Individual modules communicate locally and provably achieve consensus in coordinating movement in a common travel direction. In experiments with simulations and hardware prototypes, we show that robots achieve effective locomotion, irrespective of the number of modules and their connectivity which can be highly asymmetric.

We describe decentralized algorithms by which a swarm of simple, independent, autonomous robots can build two-dimensional structures using square building blocks. These structures can (1) exactly match arbitrary user-specified designs, (2) adapt their shape to immovable obstacles, or (3) form a wall of given minimum width around an environmental feature. These three possibilities span the range from entirely prespecified structures to those whose shape is entirely determined by the environment. Robots require no explicit communication, instead using information storage capabilities of environmental elements (a form of "extended stigmergy") to coordinate their activities. We provide theoretical proof of the correctness of the algorithms for the first two types of structures, and experimental support for algorithms for the third.

We describe a system in which autonomous robots assemble two-dimensional structures out of square building blocks. A fixed set of local control rules is sufficient for a group of robots to collectively build arbitrary solid structures. We present and compare four versions in which blocks are (1) inert and indistinguishable, (2) uniquely labeled, (3) able to be relabeled by robots, (4) capable of some computation and local communication. Added block capabilities increase the availability of nonlocal structural knowledge, thereby increasing robustness and significantly speeding construction. In this way we extend the principle of stigmergy (storing information in the environment) used by social insects, by increasing the capabilities of the blocks that represent that environmental information. Finally, we describe hardware experiments using a prototype capable of building arbitrary solid 2-D structures

Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are sometimes used to overcome these difficulties. We analyze three online training methods used with a linear perceptron: direct gradient descent, node perturbation, and weight perturbation. Learning speed is defined as the rate of exponential decay in the learning curves. When the scalar parameter that controls the size of weight updates is chosen to maximize learning speed, node perturbation is slower than direct gradient descent by a factor equal to the number of output units; weight perturbation is slower still by an additional factor equal to the number of input units. Parallel perturbation allows faster learning than sequential perturbation, by a factor that does not depend on network size. We also characterize how uncertainty in quantities used in the stochastic updates affects the learning curves. This study suggests that in practice, weight perturbation may be slow for large networks, and node perturbation can have performance comparable to that of direct gradient descent when there are few output units. However, these statements depend on the specifics of the learning problem, such as the input distribution and the target function, and are not universally applicable.

Dept. of Brain & Cog. Sci. Cambridge, MA 02139

Coevolution, between a population of candidate solutions and a population of test cases, has received increasing attention as a promising biologically inspired method for improving the performance of evolutionary computation techniques. However, the results of studies of coevolution have been mixed. One of the seemingly more impressive results to date was the improvement via coevolution demonstrated by Juille and Pollack (1998) on evolving cellular automata to perform a classification task. Their study, however, like most other studies on coevolution, did not investigate the mechanisms giving rise to the observed improvements. In this paper, we probe more deeply into the reasons for these observed improvements and present empirical evidence that, in contrast to what was claimed by Juille and Pollack, much of the improvement seen was due to their "resource sharing" technique rather than to coevolution. We also present empirical evidence that resource sharing works, at least in part, by preserving diversity in the population.