Designing Emergence Laboratory

Werfel, Justin, Kirstin Petersen, and Radhika Nagpal. 2014. “Designing Collective Behavior in a Termite-Inspired Robot Construction Team.” Science 343 (6172): 754-758. Publisher's Version Abstract
Complex systems are characterized by many independent components whose low-level actions produce collective high-level results. Predicting high-level results given low-level rules is a key open challenge; the inverse problem, finding low-level rules that give specific outcomes, is in general still less understood. We present a multi-agent construction system inspired by mound-building termites, solving such an inverse problem. A user specifies a desired structure, and the system automatically generates low-level rules for independent climbing robots that guarantee production of that structure. Robots use only local sensing and coordinate their activity via the shared environment. We demonstrate the approach via a physical realization with three autonomous climbing robots limited to onboard sensing. This work advances the aim of engineering complex systems that achieve specific human-designed goals.

For free access without a subscription, please follow the links here.
Werfel, Justin, Donald E. Ingber, and Yaneer Bar-Yam. 2015. “Programmed Death is Favored by Natural Selection in Spatial Systems.” Physical Review Letters 114: 238103. Publisher's Version Abstract
Standard evolutionary theories of aging and mortality, implicitly based on mean-field assumptions, hold that programed mortality is untenable, as it opposes direct individual benefit. We show that in spatial models with local reproduction, programed deaths instead robustly result in long-term benefit to a lineage, by reducing local environmental resource depletion via spatiotemporal patterns causing feedback over many generations. Results are robust to model variations, implying that direct selection for shorter life span may be quite widespread in nature.
Green, Ben, Paul Bardunias, J. Scott Turner, Radhika Nagpal, and Justin Werfel. 2017. “Excavation and aggregation as organizing factors in de novo construction by mound-building termites.” Proceedings of the Royal Society B 284 (1856): 20162730. Abstract
Termites construct complex mounds that are orders of magnitude larger than any individual and fulfill a variety of functional roles. Yet the processes through which these mounds are built, and by which the insects organize their efforts, remain poorly understood. The traditional understanding focuses on stigmergy, a form of indirect communication in which actions that change the environment provide cues that influence future work. Termite construction has long been thought to be organized via a putative "cement pheromone": a chemical added to deposited soil that stimulates further deposition in the same area, thus creating a positive feedback loop whereby coherent structures are built up. To investigate the detailed mechanisms and behaviors through which termites self-organize the early stages of mound construction, we tracked the motion and behavior of major workers from two Macrotermes species in experimental arenas. Rather than a construction process focused on accumulation of depositions, as models based on cement pheromone would suggest, our results indicated that the primary organizing mechanisms were based on excavation. Digging activity was focused on a small number of excavation sites, which in turn provided templates for soil deposition. This behavior was mediated by a mechanism of aggregation, with termites being more likely to join in the work at an excavation site as the number of termites presently working at that site increased. Statistical analyses showed that this aggregation mechanism was a response to active digging, distinct from and unrelated to putative chemical cues that stimulate deposition. Agent-based simulations quantitatively supported the interpretation that the early stage of de novo construction is primarily organized by excavation and aggregation activity rather than by stigmergic deposition.
Grun, Casey, Justin Werfel, David Yu Zhang, and Peng Yin. 2015. “DyNAMiC Workbench: an integrated development environment for dynamic DNA nanotechnology.” Journal of the Royal Society Interface 12 (111). Publisher's Version Abstract
Dynamic DNA nanotechnology provides a promising avenue for implementing sophisticated assembly processes, mechanical behaviours, sensing and computation at the nanoscale. However, design of these systems is complex and error-prone, because the need to control the kinetic pathway of a system greatly increases the number of design constraints and possible failure modes for the system. Previous tools have automated some parts of the design workflow, but an integrated solution is lacking. Here, we present software implementing a three ‘tier’ design process: a high-level visual programming language is used to describe systems, a molecular compiler builds a DNA implementation and nucleotide sequences are generated and optimized. Additionally, our software includes tools for analysing and ‘debugging’ the designs in silico, and for importing/exporting designs to other commonly used software systems. The software we present is built on many existing pieces of software, but is integrated into a single package—accessible using a Web-based interface at http://molecular-systems.net/workbench. We hope that the deep integration between tools and the flexibility of this design process will lead to better experimental results, fewer experimental design iterations and the development of more complex DNA nanosystems.
Werfel, Justin. 2014. “Embodied Teachable Agents: Learning by Teaching Robots.” New Research Frontiers for Intelligent Autonomous Systems (NRF-IAS-2014). PDF Abstract
Robots have an untapped potential to contribute to education by acting as subordinate learners for students to teach. The benefits that the act of teaching provides for one’s own learning have long been recognized; tutoring-associated improvements in measures like achievement scores, depth of understanding, and motivation are often far greater for the tutor than the tutee. Artificial agents can help students reap these benefits by providing surrogate pupils for them to teach, with potential advantages over human tutees. Robots have been observed to be more effective and compelling than virtual agents in a variety of contexts. However, research on teachable agents for education has been limited to virtual agents, while research on humans teaching robots has been concerned with learning for the benefit of the robot rather than that of the human. A new research direction exploring robots as teachable agents will lead to widespread benefits in education, and open new possibilities enabled by physical embodiment.

Designing Emergence Laboratory

Latest News

WBUR On Point

Wild Robots

New Doctor

WSJ on RETHi

Bioinspiration webinar

Root joins iRobot

Featured Publications