Daniel Morton


Email: dmorton at stanford dot edu

Daniel Morton

Daniel Morton is a PhD student in Mechanical Engineering. His research focuses on efficient planning and modeling methods for robotic manipulation, with an emphasis on space and novel hardware platforms. Prior to Stanford, Daniel received his B.S. in Mechanical Engineering with an Aerospace minor from Cornell University. While there, he conducted research in the Organic Robotics Lab, working on smart structures for morphing soft-robotic wings. Daniel has also interned at Boeing and NASA, and was an early member of a robotics startup, Medra. Outside of the lab, Daniel can be typically found on the tennis court or golf course, or skiing in Tahoe.


  • NSF Graduate Research Fellowship, 2022
  • NASA Space Technology Graduate Research Opportunities Fellowship, 2024

ASL Publications

  1. D. Morton, M. Cutkosky, and M. Pavone, “Task-Driven Manipulation with Reconfigurable Parallel Robots,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2024. (Submitted)

    Abstract: ReachBot, a proposed robotic platform, employs extendable booms as limbs for mobility in challenging environments, such as martian caves. When attached to the environment, ReachBot acts as a parallel robot, with reconfiguration driven by the ability to detach and re-place the booms. This ability enables manipulation-focused scientific objectives: for instance, through operating tools, or handling and transporting samples. To achieve these capabilities, we develop a two-part solution, optimizing for robustness against task uncertainty and stochastic failure modes. First, we present a mixed-integer stance planner to determine the positioning of ReachBot’s booms to maximize the task wrench space about the nominal point(s). Second, we present a convex tension planner to determine boom tensions for the desired task wrenches, accounting for the probabilistic nature of microspine grasping. We demonstrate improvements in key robustness metrics from the field of dexterous manipulation, and show a large increase in the volume of the manipulation workspace. Finally, we employ Monte-Carlo simulation to validate the robustness of these methods, demonstrating good performance across a range of randomized tasks and environments, and generalization to cable-driven morphologies. We make our code available at our project webpage, https://stanfordasl.github.io/reachbot_manipulation

      author = {Morton, Daniel and Cutkosky, Mark and Pavone, Marco},
      title = {Task-Driven Manipulation with Reconfigurable Parallel Robots},
      booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
      year = {2024},
      month = mar,
      note = {Submitted},
      keywords = {sub},
      url = {https://arxiv.org/pdf/2403.10768.pdf},
      owner = {dmorton},
      timestamp = {2024-03-16}