Hi there! I am a final-year PhD student working at the Robotics Systems Lab, TU Wien, under the supervision of Prof. Christian Ott. My research focuses on combining optimization-based control with reinforcement learning to enable safe physical interaction of underactuated aerial manipulators in uncertain environments. Previously, I was a research engineer in Prof. Antonio Franchi’s group at the University of Twente, working on model predictive control for aerial manipulation systems. I completed a MSc in Aerospace Engineering from the Delft University of Technology, with a specialization in control and simulation.
Selected Publications
Energy-based Swing-up and Grasping Maneuver using Suspended Aerial Manipulator
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026
In this work, we demonstrate a swing-up and grasping maneuver for the suspended-aerial manipulation platform. The swing-up motion of the platform is envisioned for target applications where its propulsion units cannot generate sufficient thrust to reach a desired pose. An energy regulation approach is employed to drive the suspended aerial manipulator platform to the desired limit cycle. Once the periodic oscillations are achieved, the designed online motion generator commands the manipulator to approach and grasp the given target. In this work, we employ a control strategy in which the controllers for the aerial platform and the manipulator are designed independently, while considering the dynamic coupling between the two subsystems. Our proposed approach relies on onboard sensors and a camera-tracking system, and is validated through extensive simulation studies and experimental tests.
@inproceedings{das2026energy,
title = {Energy-based Swing-up and Grasping Maneuver using Suspended Aerial Manipulator},
author = {Das, Hemjyoti and Deimel, Stefan and Ott, Christian},
booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2026}
}
Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework
In IFAC-PapersOnLine, 2025
In this work, we present a novel approach to augment a model-based control method with a reinforcement learning (RL) agent and demonstrate a swing-up maneuver with a suspended aerial manipulation platform. These platforms are targeted towards a wide range of applications on construction sites involving cranes, with swing-up maneuvers allowing it to perch at a given location, inaccessible with purely the thrust force of the platform. Our proposed approach is based on a hierarchical control framework, which allows different tasks to be executed according to their assigned priorities. An RL agent is then subsequently utilized to adjust the reference set-point of the lower-priority tasks to perform the swing-up maneuver, which is confined in the nullspace of the higher-priority tasks, such as maintaining a specific orientation and position of the end-effector. Our approach is validated using extensive numerical simulation studies.
@article{das2025learning,
title = {Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework},
author = {Das, Hemjyoti and Vu, Minh Nhat and Ott, Christian},
url = {http://dx.doi.org/10.1016/j.ifacol.2025.10.207},
journal = {IFAC-PapersOnLine},
volume = {59},
number = {18},
pages = {121–126},
publisher = {Elsevier BV},
year = {2025},
ISSN = {2405-8963}
}
Whole-Body Stabilization of a Cable-Suspended Multirotor Platform Carrying a Slung Load
In IEEE Transactions on Automation Science and Engineering (T-ASE), 2025
Suspended multirotor platforms are fascinating systems that can be employed in construction applications to provide safe transportation of heavy loads. Such a system comprising a cable-suspended platform with attached load features seven degrees of freedom (DoF) motion for the whole system. In this paper, we propose a composite whole-body control framework for the stabilization of the suspended multirotor platform system, leveraging singular perturbation theory to exploit the inherent three time-scale dynamics of the system. The control strategy computes the underactuated 3-DoF wrench space generated by the platform’s actuation units for the stabilization of the complete system. Building upon this, we develop a superposition-based shared control approach and then compare the two controllers. Moreover, to address specific cases where the time-scale separation between two dynamics of the triple-spherical pendulum becomes negligible, we design an operational space controller. The control approaches are validated using both extensive numerical simulations and experiments in different scenarios. We also carried out numerical robustness and stability analysis of the whole system. Note that our system relies on only onboard sensors for state estimation, which makes it effective for real-life outdoor applications.
@article{das2025whole,
title = {Whole-Body Stabilization of a Cable-Suspended Multirotor Platform Carrying a Slung Load},
author = {Das, Hemjyoti and Zambella, Grazia and Ott, Christian},
url = {http://dx.doi.org/10.1109/TASE.2025.3588767},
journal = {IEEE Transactions on Automation Science and Engineering},
volume = {22},
pages = {22337–22355},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
year = {2025},
ISSN = {1558-3783}
}
Nonlinear model predictive control for human-robot handover with application to the aerial case
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
In this article, we consider the problem of delivering an object to a human coworker by means of an aerial robot (AR). To this aim, we present an ergonomics-aware Nonlinear Model Predictive Control (NMPC) designed to autonomously perform the handover. The method is general enough to be applied to any multi-rotor aerial vehicle (MRAV) with a minimal adaptation of the robot model. The formulation of the optimal control problem steers the AR toward a handover location by optimizing the human coworker ergonomics, which includes the predicted arm joint torques of the human. The motion task is expressed in a frame relative to the human, whose motion model is included in the equations of the NMPC. This allows the controller to promptly adapt to the human movements by predicting her future poses over the horizon. The control framework also accounts for the problem of maintaining visibility on the human coworker, while respecting both the actuation and state limits of the robot. Additionally, a safety barrier is embedded in the controller to avoid any risk of collision with the human partner. Realistic simulations are performed to validate the feasibility of the approach and the source code of the implementation is released open-source.
@inproceedings{corsini2022nonlinear,
title = {Nonlinear Model Predictive Control for Human-Robot Handover with Application to the Aerial Case},
author = {Corsini, Gianluca and Jacquet, Martin and Das, Hemjyoti and Afifi, Amr and Sidobre, Daniel and Franchi, Antonio},
url = {http://dx.doi.org/10.1109/IROS47612.2022.9981045},
booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {7597–7604},
publisher = {IEEE},
year = {2022}
}
Motor-level N-MPC for cooperative active perception with multiple heterogeneous UAVs
In IEEE Robotics and Automation Letters, 2022
This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors – including the motor-level actuation units and their real constraints in terms of maximum torque – and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV – including both quadrotors and tilted-propeller multirotors – and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source.
@article{jacquet2022motor,
title = {Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs},
author = {Jacquet, Martin and Kivits, Max and Das, Hemjyoti and Franchi, Antonio},
url = {http://dx.doi.org/10.1109/LRA.2022.3143218},
journal = {IEEE Robotics and Automation Letters},
volume = {7},
number = {2},
pages = {2063–2070},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
year = {2022},
ISSN = {2377-3774}
}
Education
- PhD in Electrical Engineering from Vienna University of Technology, Austria (2022 - Present)
- Master of Science in Aerospace Engineering from Delft University of Technology, The Netherlands (2018 - 2020)
- Bachelor of Technology (B.Tech) in Electrical Engineering from National Institute of Technology, Silchar, India (2012 - 2016)
Experience
- PhD Researcher at Vienna University of Technology, Austria (2022 - Present)
- Research Engineer at University of Twente, The Netherlands (2020 - 2022)
- Research Internship at German Aerospace Center (DLR), Germany (2019)
- Project Engineer at Indian Institute of Science, India (2017-2018)
- Project Engineer at Indian Institute of Technology, Bombay, India (2016)