Modular Robot Design Synthesis with Deep Reinforcement Learning


  • Julian Whitman Carnegie Mellon University
  • Raunaq Bhirangi Carnegie Mellon University
  • Matthew Travers Carnegie Mellon University
  • Howie Choset Carnegie Mellon University



Modular robots hold the promise of versatility in that their components can be re-arranged to adapt the robot design to a task at deployment time. Even for the simplest designs, determining the optimal design is exponentially complex due to the number of permutations of ways the modules can be connected. Further, when selecting the design for a given task, there is an additional computational burden in evaluating the capability of each robot, e.g., whether it can reach certain points in the workspace. This work uses deep reinforcement learning to create a search heuristic that allows us to efficiently search the space of modular serial manipulator designs. We show that our algorithm is more computationally efficient in determining robot designs for given tasks in comparison to the current state-of-the-art.




How to Cite

Whitman, J., Bhirangi, R., Travers, M., & Choset, H. (2020). Modular Robot Design Synthesis with Deep Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(06), 10418-10425.



AAAI Technical Track: Robotics