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.
Awards:
- NSF Graduate Research Fellowship, 2022
- NASA Space Technology Graduate Research Opportunities Fellowship, 2024
ASL Publications
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D. Morton and M. Pavone, “Safe, Task-Consistent Manipulation with Operational Space Control Barrier Functions,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2025. (Submitted)
Abstract: Safe real-time control of robotic manipulators in unstructured environments requires handling numerous safety constraints without compromising task performance. Traditional approaches, such as artificial potential fields (APFs), suffer from local minima, oscillations, and limited scalability, while model predictive control (MPC) can be computationally expensive. Control barrier functions (CBFs) offer a promising alternative due to their high level of robustness and low computational cost, but these safety filters must be carefully designed to avoid significant reductions in the overall performance of the manipulator. In this work, we introduce an Operational Space Control Barrier Function (OSCBF) framework that integrates safety constraints while preserving task-consistent behavior. Our approach scales to hundreds of simultaneous constraints while retaining real-time control rates, ensuring collision avoidance, singularity prevention, and workspace containment even in highly cluttered and dynamic settings. By explicitly accounting for the task hierarchy in the CBF objective, we prevent degraded performance across both joint-space and operational-space tasks, when at the limit of safety. Our open-source, high-performance software will be available at our project webpage, https://stanfordasl.github.io/oscbf/
@inproceedings{MortonPavone2025, author = {Morton, D. and Pavone, M.}, title = {Safe, Task-Consistent Manipulation with Operational Space Control Barrier Functions}, booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}}, year = {2025}, keywords = {sub}, url = {https://arxiv.org/pdf/2503.06736}, owner = {dmorton}, timestamp = {2025-03-11} } -
M. Ganai, R. Sinha, C. Agia, D. Morton, and M. Pavone, “Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning,” in Conf. on Robot Learning, 2025. (Submitted)
Abstract: Foundation models can provide robust high-level reasoning on appropriate safety interventions in hazardous scenarios beyond a robot’s training data, i.e. out-of-distribution (OOD) failures. However, due to the high inference latency of Large Vision and Language Models, current methods rely on manually defined intervention policies to enact fallbacks, thereby lacking the ability to plan generalizable, semantically safe motions. To overcome these challenges we present FORTRESS, a framework that generates and reasons about semantically safe fallback strategies in real time to prevent OOD failures. At a low frequency in nominal operations, FORTRESS uses multi-modal reasoners to identify goals and anticipate failure modes. When a runtime monitor triggers a fallback response, FORTRESS rapidly synthesizes plans to fallback goals while inferring and avoiding semantically unsafe regions in real time. By bridging open-world, multi-modal reasoning with dynamics-aware planning, we eliminate the need for hard-coded fallbacks and human safety interventions. FORTRESS outperforms on-the-fly prompting of slow reasoning models in safety classification accuracy on synthetic benchmarks and real-world ANYmal robot data, and further improves system safety and planning success in simulation and on quadrotor hardware for urban navigation. Website can be found at https://submfort.github.io/fortress/
@inproceedings{GanaiSinhaEtAl2025, author = {Ganai, M. and Sinha, R. and Agia, C. and Morton, D. and Pavone, M.}, title = {Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning}, booktitle = {{Conf. on Robot Learning}}, year = {2025}, owner = {mganai}, note = {Submitted}, keywords = {sub}, url = {https://arxiv.org/abs/2505.10547}, timestamp = {2025-06-08} } -
D. Morton, M. Cutkosky, and M. Pavone, “Task-Driven Manipulation with Reconfigurable Parallel Robots,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2024.
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.
@inproceedings{MortonCutkoskyPavone2024, author = {Morton, D. and Cutkosky, M. and Pavone, M.}, title = {Task-Driven Manipulation with Reconfigurable Parallel Robots}, booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}}, year = {2024}, keywords = {pub}, url = {https://arxiv.org/pdf/2403.10768.pdf}, owner = {dmorton}, timestamp = {2024-03-16} } -
K. Spieser, K. Treleaven, R. Zhang, E. Frazzoli, D. Morton, and M. Pavone, “Toward a Systematic Approach to the Design and Evaluation of Autonomous Mobility-on-Demand Systems: A Case Study in Singapore,” in Road Vehicle Automation, Springer, 2014.
Abstract: The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately one third of the total number of passenger vehicles currently in operation.
@incollection{SpieserTreleavenEtAl2014, author = {Spieser, K. and Treleaven, K. and Zhang, R. and Frazzoli, E. and Morton, D. and Pavone, M.}, title = {Toward a Systematic Approach to the Design and Evaluation of {Autonomous} {Mobility-on-Demand} Systems: A Case Study in {Singapore}}, booktitle = {Road Vehicle Automation}, year = {2014}, doi = {10.1007/978-3-319-05990-7_20}, url = {http://dspace.mit.edu/handle/1721.1/82904}, owner = {bylard}, publisher = {{Springer}}, timestamp = {2017-06-15} }