About

Junyi Geng is a faculty member within Penn State's Department of Aerospace Engineering, which has a distinguished record of research excellence and scholarship. The department's research spans traditional disciplines associated with aeronautics and astronautics, with particular strengths in rotorcraft and aero-acoustics. The department is also expanding into new areas driven by increasing computational power for design, analysis, on-board autonomy, sustainable aviation, and space systems. While the specific research focus of Junyi Geng is not detailed in the provided text, the department's broad research activities include leadership in vertical lift and rotorcraft, involvement in large multidisciplinary efforts such as NASA initiatives, and recognition through numerous awards and fellowships. The department's faculty, including junior faculty with NSF CAREER and DoD Young Investigator awards, contribute to a vibrant research environment that advances aerospace technology and knowledge.

Research topics

• Computer Science
• Artificial Intelligence
• Physics
• Mathematics
• Simulation
• Classical mechanics
• Statistics
• Acoustics
• Algorithm
• Human–computer interaction
• Computer vision
• Engineering
• Aerospace engineering
• Control engineering
• Mechanics

Selected publications

• Physiological and Transcriptomic Responses of Sunflower to Combined Saline–Alkali Stress

  Research Square · 2026-03-11

  preprintOpen access
  PublisherOA PDFDOI

• Physics-infused Learning for Aerial Manipulator in Winds and Near-Wall Environments

  arXiv (Cornell University) · 2026-03-08

  preprintOpen accessSenior author

  Aerial manipulation (AM) expands UAV capabilities beyond passive observation to contact-based operations at high altitudes and in otherwise inaccessible environments. Although recent advances show promise, most AM systems are developed in controlled settings that overlook key aerodynamic effects. Simplified thrust models are often insufficient to capture the nonlinear wind disturbances and proximity-induced flow variations present in real-world environments near infrastructure, while high-fidelity CFD methods remain impractical for real-time use. Learning-based models are computationally efficient at inference, but often struggle to generalize to unseen condition. This paper combines both approaches by integrating a physics-based blade-element model with a learning-based residual force estimator, along with a rotor-speed allocation strategy for disturbance compensation, resulting in a unified control framework. The blade-element model computes per-rotor aerodynamic forces under wind and provides a refined feedforward disturbance estimate. A learning-based estimator then predicts the residual forces not captured by the model, enabling compensation for unmodeled aerodynamic effects. An online adaptation mechanism further updates the residual-force prediction and rotor-speed allocation jointly to reduce the mismatch between desired and realized thrust. We evaluate this framework in both free-flight and wall-contact tracking tasks in a simulated near-wall wind environment. Results demonstrate improved disturbance estimation and trajectory-tracking accuracy over conventional approaches, enabling robust wall-contact execution under challenging aerodynamic conditions.

  PublisherDOI

• Piecewise-Affine Jump State Estimator Design Within Reachable Target Regions Under Noisy Measurements

  2025-06-30

  article

  The paper concerns state estimation of discretetime jump systems using piecewise-affine (PWA) modeling. Since certain regions are unreachable in one time step, we present an algorithm to identify reachable regions at the next time step for both the actual state and its estimation under noisy measurements. The stability and noise attenuation of an estimation error system are analyzed by excluding unreachable regions via a mode-dependent piecewise Lyapunov function. A mode-dependent PWA estimator is then designed to ensure stability and fulfill the noise attenuation requirement with lower computational cost. In comparison, the approach based on reachable target regions reduces both computational complexity and conservativeness [1]. The proposed state estimation approach is validated to be advantageous through its application to a tunnel diode circuit.

  PublisherDOI

• Imperative learning: A self-supervised neuro-symbolic learning framework for robot autonomy

  The International Journal of Robotics Research · 2025-08-05 · 6 citations

  preprintOpen access

  Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from generalizing well to ever-changing environments. Moreover, labeling data for robotic tasks is often impractical and expensive. To overcome these challenges, we introduce a new self-supervised neuro-symbolic (NeSy) computational framework, imperative learning (IL), for robot autonomy, leveraging the generalization abilities of symbolic reasoning. The framework of IL consists of three primary components: a neural module, a reasoning engine, and a memory system. We formulate IL as a special bilevel optimization (BLO), which enables reciprocal learning over the three modules. This overcomes the label-intensive obstacles associated with data-driven approaches and takes advantage of symbolic reasoning concerning logical reasoning, physical principles, geometric analysis, etc. We discuss several optimization techniques for IL and verify their effectiveness in five distinct robot autonomy tasks including path planning, rule induction, optimal control, visual odometry, and multi-robot routing. Through various experiments, we show that IL can significantly enhance robot autonomy capabilities and we anticipate that it will catalyze further research across diverse domains.

  PublisherOA PDFDOI

• A Self-Supervised Learning Approach with Differentiable Optimization for UAV Trajectory Planning

  ArXiv.org · 2025-04-05

  preprintOpen accessSenior author

  While Unmanned Aerial Vehicles (UAVs) have gained significant traction across various fields, path planning in 3D environments remains a critical challenge, particularly under size, weight, and power (SWAP) constraints. Traditional modular planning systems often introduce latency and suboptimal performance due to limited information sharing and local minima issues. End-to-end learning approaches streamline the pipeline by mapping sensory observations directly to actions but require large-scale datasets, face significant sim-to-real gaps, or lack dynamical feasibility. In this paper, we propose a self-supervised UAV trajectory planning pipeline that integrates a learning-based depth perception with differentiable trajectory optimization. A 3D cost map guides UAV behavior without expert demonstrations or human labels. Additionally, we incorporate a neural network-based time allocation strategy to improve the efficiency and optimality. The system thus combines robust learning-based perception with reliable physics-based optimization for improved generalizability and interpretability. Both simulation and real-world experiments validate our approach across various environments, demonstrating its effectiveness and robustness. Our method achieves a 31.33% improvement in position tracking error and 49.37% reduction in control effort compared to the state-of-the-art.

  PublisherOA PDFDOI

• AirIO: Learning Inertial Odometry With Enhanced IMU Feature Observability

  IEEE Robotics and Automation Letters · 2025-06-19 · 5 citations

  article

  Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian motion. In this work, we identify that the conventional practice of transforming raw IMU data to global coordinates undermines the observability of critical information in UAVs. By preserving the body-frame representation, our method achieves substantial performance improvements, with a 66.7% average increase in accuracy across three datasets. Furthermore, explicitly encoding attitude information into the motion network results in an additional 23.8% improvement over prior results. Combined with a data-driven IMU correction model (AirIMU) and an uncertainty-aware Extended Kalman Filter (EKF), our approach ensures robust state estimation under aggressive UAV maneuvers without relying on external sensors or control inputs. Notably, our method also demonstrates strong generalizability to unseen data, underscoring its potential for real-world UAV applications.

  PublisherDOI

• iKap: Kinematics-Aware Planning with Imperative Learning

  2025-05-19 · 2 citations

  article

  Trajectory planning in robotics aims to generate collision-free pose sequences that can be reliably executed. Recently, vision-to-planning systems have gained increasing attention for their efficiency and ability to interpret and adapt to surrounding environments. However, traditional modular systems suffer from increased latency and error propagation, while purely data-driven approaches often overlook the robot's kinematic constraints. This oversight leads to discrepancies between planned trajectories and those that are executable. To address these challenges, we propose iKap, a novel vision-toplanning system that integrates the robot's kinematic model directly into the learning pipeline. iKap employs a self-supervised learning approach and incorporates the state transition model within a differentiable bi-level optimization framework. This integration ensures the network learns collision-free waypoints while satisfying kinematic constraints, enabling gradient backpropagation for end-to-end training. Our experimental results demonstrate that iKap achieves higher success rates and reduced latency compared to the state-of-the-art methods. Besides the complete system, iKap offers a visual-to-planning network that seamlessly works with various controllers, providing a robust solution for robots navigating complex environments.

  PublisherDOI

• [Withdrawn] AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability —  air-io.github.io

  Qeios · 2025-02-05

  preprintOpen access

  This manuscript has been withdrawn.

  PublisherOA PDFDOI

• Flying Hand: End-Effector-Centric Framework for Versatile Aerial Manipulation Teleoperation and Policy Learning

  ArXiv.org · 2025-04-14

  preprintOpen access

  Aerial manipulation has recently attracted increasing interest from both industry and academia. Previous approaches have demonstrated success in various specific tasks. However, their hardware design and control frameworks are often tightly coupled with task specifications, limiting the development of cross-task and cross-platform algorithms. Inspired by the success of robot learning in tabletop manipulation, we propose a unified aerial manipulation framework with an end-effector-centric interface that decouples high-level platform-agnostic decision-making from task-agnostic low-level control. Our framework consists of a fully-actuated hexarotor with a 4-DoF robotic arm, an end-effector-centric whole-body model predictive controller, and a high-level policy. The high-precision end-effector controller enables efficient and intuitive aerial teleoperation for versatile tasks and facilitates the development of imitation learning policies. Real-world experiments show that the proposed framework significantly improves end-effector tracking accuracy, and can handle multiple aerial teleoperation and imitation learning tasks, including writing, peg-in-hole, pick and place, changing light bulbs, etc. We believe the proposed framework provides one way to standardize and unify aerial manipulation into the general manipulation community and to advance the field. Project website: https://lecar-lab.github.io/flying_hand/.

  PublisherOA PDFDOI

• AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability

  ArXiv.org · 2025-01-26

  preprintOpen access

  Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian motion. In this work, we identify that the conventional practice of transforming raw IMU data to global coordinates undermines the observability of critical kinematic information in UAVs. By preserving the body-frame representation, our method achieves substantial performance improvements, with a 66.7% average increase in accuracy across three datasets. Furthermore, explicitly encoding attitude information into the motion network results in an additional 23.8% improvement over prior results. Combined with a data-driven IMU correction model (AirIMU) and an uncertainty-aware Extended Kalman Filter (EKF), our approach ensures robust state estimation under aggressive UAV maneuvers without relying on external sensors or control inputs. Notably, our method also demonstrates strong generalizability to unseen data not included in the training set, underscoring its potential for real-world UAV applications.

  PublisherOA PDFDOI

Frequent coauthors

• Sebastian Scherer

  18 shared

• Mohammadreza Mousaei

  Carnegie Mellon University

  13 shared

• Katherine Driggs-Campbell

  9 shared

• Guanqi He

  Carnegie Mellon University

  9 shared

• Jack W. Langelaan

  Pennsylvania State University

  8 shared

• Chen Wang

  University at Buffalo, State University of New York

  8 shared

• Zhe Huang

  6 shared

• Weihang Liang

  5 shared

Labs

• Aerospace Engineering Research at Penn StatePI

Education

• Ph.D, Aerospace Engineering

  Pennsylvania State University

• M.S., Aerospace Engineering

  Pennsylvania State University

  2016

• B.E., Aircraft Design and Engineering

  Nanjing University of Aeronautics and Astronautics

  2013

Awards & honors

• AIAA Aero Acoustics Award (3)
• AIAA Sperry Award (2)
• AIAA Applied Aerodynamics Award
• Am Astronautical Society Brouwer Award
• AIAA de Florez Award in Flight Simulation