KNODE-MPC: A Knowledge-Based Data-Driven Predictive Control Framework for Aerial Robots

K. Y. Chee, T. Z. Jiahao and M. A. Hsieh

Abstract: In this letter, we consider the problem of deriving and incorporating accurate dynamic models for model predictive control (MPC) with an application to quadrotor control. MPC relies on precise dynamic models to achieve the desired closed-loop performance. However, the presence of uncertainties in complex systems and the environments they operate in poses a challenge in obtaining sufficiently accurate representations of the system dynamics. In this letter, we make use of a deep learning tool, knowledge-based neural ordinary differential equations (KNODE), to augment a model obtained from first principles. The resulting hybrid model encompasses both a nominal first-principle model and a neural network learnt from simulated or real-world experimental data. Using a quadrotor, we benchmark our hybrid model against a state-of-the-art Gaussian Process (GP) model and show that the hybrid model provides more accurate predictions of the quadrotor dynamics and is able to generalize beyond the training data. To improve closed-loop performance, the hybrid model is integrated into a novel MPC framework, known as KNODE-MPC. Results show that the integrated framework achieves 60.2% improvement in simulations and more than 21% in physical experiments, in terms of trajectory tracking performance.

Status: Published in IEEE RA-L.

Preprint available on arXiv:

Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness

T. Salam, V. Edwards, and M. A. Hsieh

Abstract: This letter studies how global dynamics and knowledge of high-level features can inform decision-making for robots in flow-like environments. Specifically, we investigate how coherent sets, an environmental feature found in these environments, inform robot awareness within these scenarios. The proposed approach is an online environmental feature generator which can be used for robot reasoning. We compute coherent sets online with techniques from machine learning and design frameworks for robot behavior that leverage coherent set features. We demonstrate the effectiveness of online methods over offline methods. Notably, we apply these online methods for robot monitoring of pedestrian behaviors and robot navigation through water. Environmental features such as coherent sets provide rich context to robots for smarter, more efficient behavior.

Status: Published in IEEE RA-L.

Preprint available on arXiv:

Come see our work at ICRA 2022!

Come and see our presentations at ICRA 2022 next week (May 23-27)!

  • Tahiya Salam, Sandeep Manjanna, Ani Hsieh, and Gregory Dudek from McGill University will be hosting the Robotics for Climate Change Workshop on Monday in Room 108B. See here for more information!
  • Kong Yao Chee will be presenting our KNODE-MPC work in the Motion Control Session (TuA16) on Tuesday morning.  His talk is TuA16.07 and starts at 10:45 in Room 122B.
  • Gedaliah Knizhnik will be presenting his work with Mark Yim on control for docking Modboats in the Marine Robotics/Localization Session (TuB13) on Tuesday afternoon. His talk is TuB13.01 and starts at 15:30 in Room 116.
  • Peihan Li will be presenting our work on controlling Modboats in ocean-like environments in the Marine Robotics/Localization Session (TuB13) on Tuesday afternoon. His talk is TuB13.04 and starts at 15:45 in Room 116.
  • Tahiya Salam will be presenting our work on learning transfer operators using kernel methods in the Industrial and Environmental Robotics and Monitoring Session (WeA13) on Wednesday morning. Her talk is WeA13.01 and starts at 10:00 in Room 116.
  • Tom Zhang will be presenting our work with learning swarming controllers using KNODE in the Multi-Robot and Swarm Robotics II Session (WeB12) on Wednesday afternoon. His talk is WeB12.09 and starts at 16:20 in Room 118C.
  • Come see Jasleen Dhanoa and her MEAM 520 Fall 2022 cohorts demoing their MEAM 520 final project at the GRASP Exhibit Booth in the Exhibit Hall Tue-Thu!

Congratulations to ScaLAR member Ariella Mansfield and GRASPees Rebecca Li and Michael Sobrepera on winning Penn Health-Tech’s Rothberg Catalyzer

Ariella Mansfield, Rebecca Li, and Michael Soprepera won the Penn Health-Tech’s Rothberg Catalyzer, a two-day makerthon that challenges interdisciplinary student teams to prototype and pitch medical devices that aim to address an unmet clinical need.

The team took home the top prize of $10,000 for their project, an orthotic device that children with cerebral palsy can more comfortably wear as they sleep.

Read more about the project here.

Controlling stochastic switching

Using control to shape stochastic escape and switching dynamics

Dhanushka Kularatne, Eric Forgoston, and M. Ani Hsieh

Abstract: We present a strategy to control the mean stochastic switching times of general dynamical systems with multiple equilibrium states subject to Gaussian white noise. The control can either enhance or abate the probability of escape from the deterministic region of attraction of a stable equilibrium in the presence of external noise. We synthesize a feedback control strategy that actively changes the system’s mean stochastic switching behavior based on the system’s distance to the boundary of the attracting region. With the proposed controller, we are able to achieve a desired mean switching time, even when the strength of noise in the system is not known. The control method is analytically validated using a one-dimensional system, and its effectiveness is numerically demonstrated for a set of dynamical systems of practical importance.

Status: Published online. doi: 10.1063/1.5090113