About me

I’m an incoming PhD student at the Agile Robotics and Perception Lab (ARPL), New York University. My research interests pertain to developing algorithms to facilitate autonomous navigation of aerial vehicles, particularily in the wild. I obtained my M.S. degree from New York University and B.S. degree from Manipal Institute of Technology.

Research & Publications

	Learning Long-Horizon Predictions for Quadrotor Dynamics Pratyaksh Prabhav Rao, Alessandro Saviolo, Tommaso Castiglione Ferrari, and Giuseppe Loianno. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, Abu Dhabi (Accepted) [paper] Accurate modeling of system dynamics is crucial for achieving high-performance planning and control of robotic systems. Although existing data-driven approaches represent a promising approach for modeling dynamics, their accuracy is limited to a short prediction horizon, overlooking the impact of compounding prediction errors over longer prediction horizons. Strategies to mitigate these cumulative errors remain underexplored. To bridge this gap, in this paper, we study the key design choices for efficiently learning long-horizon prediction dynamics for quadrotors. Specifically, we analyze the impact of multiple architectures, historical data, and multi-step loss formulation. We show that sequential modeling techniques showcase their advantage in minimizing compounding errors compared to other types of solutions. Furthermore, we propose a novel decoupled dynamics learning approach, which further simplifies the learning process while also enhancing the approach modularity.Extensive experiments and ablation studies on real-world quadrotor data demonstrate the versatility and precision of the proposed approach. Our outcomes offer several insights and methodologies for enhancing long-term predictive accuracy of learned quadrotor dynamics for planning and control.
	QuadFormer: Real-Time Unsupervised Power Line Segmentation with Transformer-Based Domain Adaptation Pratyaksh Prabhav Rao*, Feng Qiao*, Weide Zhang, Yiliang Xu, Yong Deng, Guangbin Wu, Qiang Zhang, and Giuseppe Loianno. 21st International Conference on Ubiquitous Robots (UR), New York (Accepted) [paper] [video] Accurately identifying Power Lines (PLs) is crucial for ensuring the safety of aerial vehicles. Despite the potential of recent deep learning approaches, obtaining high-quality ground truth annotations remains a challenging and labor-intensive task. Unsupervised Domain Adaptation (UDA) emerges as a promising solution, leveraging knowledge from labeled synthetic data to improve performance on unlabeled real images. However, existing UDA methods often suffer of huge computation costs, limiting their deployment on real-time embedded systems commonly utilized on aerial vehicles. To mitigate this problem, this paper introduces QuadFormer, a real-time framework designed for unsupervised semantic segmentation within the UDA paradigm. QuadFormer integrates a lightweight transformer-based segmentation model with a cross-attention mechanism to narrow the gap between a labelled synthetic domain and unlabelled real domain. Furthermore, we design a novel pseudo label scheme to enhance the segmentation accuracy of the unlabelled real data. To facilitate the evaluation of our framework and promote reserach in PL segemntation, we present two new datasets: AutelPL Synthetic and AutelPL Real. Experimental results demonstrate that QuadFormer achieves state-of-the-art performance on both AutelPL Synthetic to TTPLA and AutelPL Synthetic to AutelPL Real tasks. We will publicly release the dataset to the research community.
	Unifying Foundation Models with Quadrotor Control for Visual Tracking Beyond Object Categories Alessandro Saviolo*, Pratyaksh Rao*, Vivek Radhakrishnan, Jiuhong Xiao, and Giuseppe Loianno. 2024 IEEE International Conference on Robotics and Automation, Yokohama [paper] [video] Visual control enables quadrotors to adaptively navigate using real-time sensory data, bridging perception with action. Yet, challenges persist, including generalization across scenarios, maintaining reliability, and ensuring real-time responsiveness. This paper introduces a perception framework grounded in foundation models for universal object detection and tracking, moving beyond specific training categories. Integral to our approach is a multi-layered tracker integrated with the foundation detector, ensuring continuous target visibility, even when faced with motion blur, abrupt light shifts, and occlusions. Complementing this, we introduce a model-free controller tailored for resilient quadrotor visual tracking. Our system operates efficiently on limited hardware, relying solely on an onboard camera and an inertial measurement unit. Through extensive validation in diverse challenging indoor and outdoor environments, we demonstrate our system’s effectiveness and adaptability. In conclusion, our research represents a step forward in quadrotor visual tracking, moving from taskspecific methods to more versatile and adaptable operations.
	Learning to Listen and Move: An Implementation of Audio-Aware Mobile Robot Navigation in Complex Indoor Environment Pratyaksh P. Rao and Abhra Roy Chowdhury. 2022 IEEE International Conference on Robotics and Automation, Philadelphia [paper] [video] Sound is an essential navigation cue that intelligent robots can leverage for localizing sound-emitting targets. This work introduces a framework for the audio-aware navigation task of mobile robots equipped with a microphone array in a complex indoor environment. The robot initialized at a random starting position has to accurately localize a distant sound source and plan an optimal path towards the sound-emitting target. Auto-encoders are used to extract implicit acoustic features that are robust against environmental noise and reverberation. The proposed framework is based on two key ideas - a sound inference module (SIM) that maps the perceived acoustic information to a given geometric map of the physical space, and a path planner that generates a path from the robot’s current position to the estimated position of the sound source. Experimental results show that the SIM achieved a minimum and maximum localization error of 0.31 m and 0.70 m at a robot-source distance of 1 m and 6 m, respectively at different environmental configurations. Additionally, the proposed framework achieved a minimum and maximum reliability of 4.38 m^1 and 2.31 m^1 at a robot-source distance of 1 m and 6 m, respectively under the influence of background noise.

Honors and Awards