Programmable Self-Adaptation

Biologically-Inspired Control for Self-Adaptive Multiagent Systems

LINKS: Movies | Publications

Biological systems can achieve scalable and adaptive group behaviors, such as animal flocking, through local interactions amongst a vast network of unreliable agents. In these systems, each agent acts autonomously and interacts only with its neighbors, yet the global system exhibits coordinated behavior. Large-scale multi-agent systems, e.g. distributed robot systems, are similar to these biological systems, in that their overall tasks must be achieved by coordinating many independent agents. One important question to ask is: How can we program large networks of agents to achieve collective tasks, and at the same time adapt to dynamic conditions like living systems?

Our group has developed a biologically-inspired control framework for multiagent networks to achieve coordinated tasks in a scalable, robust, and analyzable manner. We focus on distributed homeostasis, a type of task in which agents must use distributed sensing to solve collective tasks and to cope with changing environments. This task space can be formulated more generally as distributed constraint-maintenance on a networked multi-agent system, and this approach allows us to capture various multi-agent scenarios and tasks. We show how one can exploit the locality of this formulation to design nearest-neighbor agent control, based on simple sensing, actuation and local communication.

The main contributions and directions of this project are:

  • Simple Decentralized Algorithms for Various Self-Adaptive Tasks: We formulate this problem more generally as distributed constraint maintenance on a networked multiagent system. Such a formulation allows the framework to model and simplify tasks on a variety of distributed agent applications. It also allows us to exploit locality of the task to derive decentralized agent control that is based on a simple sensing and actuation strategy.

  • Theoretical Analysis and Understanding of the Approach: We analyze important aspects of this decentralized approach, including convergence, scalability, and robustness. We also outline sufficient conditions that guarantee agent control laws' correctness for different networked multiagent systems. This analytical framework allows one to have a concrete understanding of the strengths, limitations, and scope of this class of decentralized approaches.

  • Many Application Areas: This framework has wide applicability to autonomous systems that exploit sensor-actuator networks as underlying architectures, including modular robots, adaptive architecture,  multi-robot cooperation, and adaptive medical devices. We have designed and demonstrated a number of different hardware prototype systems, using both chain-style and link-style modular robots, based on this theoretical approach.

Chih-han Yu received the runner-up prize for the 2010 Victor Lesser Distinguished Dissertation Award for his PhD Thesis on Self-adapting Multi-agent Systems, at AAMAS 2011.



You can see various movies from the project on our SSR Youtube playlist. Here are a few movies from the project (1) Self-adapting Modular Robot Table and surface (2) A Pressure Adaptive modular robot gripper (3) Amorphous modular robot using a link-based "deformable" structure for locomotion (4) The soft orthotics project inspired by this work


Overview Articles:

Biologically-Inspired Control for Self-Adaptive Multiagent Systems
Chih-Han Yu, Doctoral Thesis, Harvard University, Apr 2010. (pdf)

Biologically-Inspired Control for Multi-Agent Self-Adaptive Tasks
Chih-Han Yu, Radhika Nagpal.
Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2010. (pdf)
New scientific and technical advances in research (Nectar) Track

Algorithms and Theories:

Collective Decision-Making in Multi-Agent Systems by Implicit Leadership
Chih-Han Yu, Justin Werfel, Radhika Nagpal.
Intl. Conf on Autonomous Agents and Multi-Agent Systems (AAMAS), 2010. (pdf)

Sensing-based Shape Formation Tasks on Modular Multi-Robot Systems: A Theoretical Study
Chih-Han Yu, Radhika Nagpal,
Intl. Conf on Autonomous Agents and Multi-Agent Systems (AAMAS), 2008. (pdf)
Nominated for Pragnesh Jay Modi Best Student Paper Award.


Coordinating Collective Locomotion in an Amorphous Modular Robot
Chih-Han Yu, Justin Werfel, Radhika Nagpal.
IEEE International Conference on Robotics and Automation (ICRA), 2010. (pdf)

Self-Adapting Modular Robotics: A Generalized Distributed Consensus Framework
Chih-Han Yu, Radhika Nagpal.
IEEE International Conference on Robotics and Automation (ICRA), 2009. (pdf)
Also see Video Paper, IROS 2009 (Video, youtube link ) (pdf)

Self-organizing Environmentally-adaptive Shapes on a Modular Robot
Chih-Han Yu, FX Williems, Donald Ingber, Radhika Nagpal
IEEE Conference on Intelligent Robots and Systems (IROS), Oct, 2007 (pdf)

Robot Hardware:

Mechanical Design and Locomotion of Modular-Expanding Robots
Rebecca Belisle, Chih-Han Yu, Radhika Nagpal.
Modular Robotics Workshop, IEEE Intl. Conf. on Robotics and Automation (ICRA), 2010. (pdf)

Morpho: A Self-Deformable Modular Robot Inspired By Cellular Structure
Chih-Han Yu, Kristina Haller, Donald Ingber, Radhika Nagpal.
IEEE Intl. Conference on Intelligent Robots and Systems (IROS), 2008. (pdf)