Contemporary sensorimotor learning approaches typically start with an existing complex agent (e.g., a robotic arm), which they learn to control. In contrast, this paper investigates a modular co-evolution strategy: a collection of primitive agents learns to dynamically self-assemble into composite bodies while also learning to coordinate their behavior to control these bodies. Each primitive agent consists of a limb with a motor attached at one end. Limbs may choose to link up to form collectives. When a limb initiates a link-up action and there is another limb nearby, the latter is magnetically connected to the 'parent' limb's motor. This forms a new single agent, which may further link with other agents. In this way, complex morphologies can emerge, controlled by a policy whose architecture is in explicit correspondence with the morphology. We evaluate the performance of these 'dynamic' and 'modular' agents in simulated environments. We demonstrate better generalization to test-time changes both in the environment, as well as in the agent morphology, compared to static and monolithic baselines. Project videos and code are available at this https URL
The idea of modular and self-assembling agents goes back at least to Von Neumman's Theory of Self-Reproducing Automata @cite_1 . In robotics, such systems have been termed self-reconfiguring modular robots" @cite_33 @cite_24 . There has been a lot of work in modular robotics to design real hardware robotic modules that can be docked together to form complex robotic morphologies @cite_3 @cite_7 @cite_32 @cite_30 @cite_9 . We approach this problem from a learning perspective, in particular deep RL, and study the resulting generalization properties.
@cite_30 @cite_33 @cite_7 @cite_9 @cite_1 @cite_32 @cite_3 @cite_24
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We describe the design, implementation and programming of a set of robots that, starting from an amorphous arrangement, can be assembled into arbitrary shapes and then commanded to self-disassemble in an organized manner to obtain a goal shape. We present custom hardware, distributed algorithms and experimental results from hundreds of trails which show the system successfully forming complex 3D shapes. Each of the 28 modules in the system is implemented as a 1.8-inch autonomous cube-shaped robot able to connect to and communicate with its immediate neighbors. Embedded microprocessors control each module's magnetic connection mechanisms and infrared communication interfaces. When assembled into a structure, the modules form a system that can be virtually sculpted using a computer interface and a distributed process. The group of modules collectively decides which elements are a part of the final shape and which are not using algorithms that minimize information transmission and storage. Finally, the modules not in the structure disengage their magnetic couplings and fall away under the influence of an external force: in this case, gravity. Self-reconfigurable robots are constructed of robotic modules that can be connected in many different ways. These modules move in relationship to each other, which allows the robot as a whole to change shape. This shapeshifting makes it possible for the robots to adapt and optimize their shapes for different tasks. Thus, a self-reconfigurable robot can first assume the shape of a rolling track to cover distance quickly, then the shape of a snake to explore a narrow space, and finally the shape of a hexapod to carry an artifact back to the starting point. The field of self-reconfigurable robots has seen significant progress over the last twenty years, and this book collects and synthesizes existing research previously only available in widely scattered individual papers, offering an accessible guide to the latest information on self-reconfigurable robots for researchers and students interested in the field. Self-Reconfigurable Robots focuses on conveying the intuition behind the design and control of self-reconfigurable robots rather than technical details. Suggestions for further reading refer readers to the underlying sources of technical information. The book includes descriptions of existing robots and a brief history of the field; discussion of module design considerations, including module geometry, connector design, and computing and communication infrastructure; an in-depth presentation of strategies for controlling self-reconfiguration and locomotion; and exploration of future research challenges. Many factors such as size, power, and weight constrain the design of modular snake robots. Meeting these constraints requires implementing a complex mechanical and electrical architecture. Here we present our solution, which involves the construction of sixteen aluminum modules and creation of the Super Servo, a modified hobby servo. To create the Super Servo, we have replaced the electronics in a hobby servo, adding such components as sensors to monitor current and temperature, a communications bus, and a programmable microcontroller. Any robust solution must also protect components from hazardous environments such as sand and brush. To resolve this problem we insert the robots into skins that cover their surface. Functions such as climbing the inside and outside of a pipe add a new dimension of interaction. Thus we attach a compliant, high-friction material to every module, which assists in tasks that require gripping. This combination of the mechanical and electrical architectures results in a robust and versatile robot. The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task. The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world. In this paper, we describe a novel self-assembling, self-reconfiguring cubic robot that uses pivoting motions to change its intended geometry. Each individual module can pivot to move linearly on a substrate of stationary modules. The modules can use the same operation to perform convex and concave transitions to change planes. Each module can also move independently to traverse planar unstructured environments. The modules achieve these movements by quickly transferring angular momentum accumulated in a self-contained flywheel to the body of the robot. The system provides a simplified realization of the modular actions required by the sliding cube model using pivoting. We describe the principles, the unit-module hardware, and extensive experiments with a system of eight modules. Modular, self-reconfigurable robots show the promise of great versatility, robustness and low cost. The paper presents examples and issues in realizing those promises. PolyBot is a modular, self-reconfigurable system that is being used to explore the hardware reality of a robot with a large number of interchangeable modules. PolyBot has demonstrated the versatility promise, by implementing locomotion over a variety of terrain and manipulation versatility with a variety of objects. PolyBot is the first robot to demonstrate sequentially two topologically distinct locomotion modes by self-reconfiguration. PolyBot has raised issues regarding software scalability and hardware dependency and as the design evolves the issues of low cost and robustness will be resolved while exploring the potential of modular, self-reconfigurable robots.