About

Ramanarayan Vasudevan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan. His research interests include the optimization, modeling, design, and control of robotic systems that interact with humans and the environment. His group works on a variety of applications such as locomotion of legged robots, prosthetic control, autonomous vehicles, and soft robots. Vasudevan has received recognition for his contributions, including the ONR 2018 Young Investigator Award, and has been involved in advancing autonomous systems and robotic locomotion through his research.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Statistics
• Mathematics
• Engineering
• Control engineering

Selected publications

• A Generalized Metriplectic System via Free Energy and System Identification via Bilevel Convex Optimization

  2025-07-08

  article

  This work generalizes the classical metriplectic formalism to model Hamiltonian systems with nonconservative dissipation. Classical metriplectic representations allow for the description of energy conservation and production of entropy via a suitable selection of an entropy function and a bilinear symmetric metric. By relaxing the Casimir invariance requirement of the entropy function, this paper shows that the generalized formalism induces the free energy analogous to thermodynamics. The monotonic change of free energy can serve as a more precise criterion than mechanical energy or entropy alone. This paper provides examples of the generalized metriplectic system in a 2-dimensional Hamiltonian system and SO(3). This paper also provides a bilevel convex optimization approach for the identification of the metriplectic system given measurements of the system.

  PublisherDOI

• TRNeRF: Restoring Blurry, Rolling Shutter, and Noisy Thermal Images with Neural Radiance Fields

  2025-02-26 · 2 citations

  article
  PublisherDOI

• RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes

  2025-10-19

  articleOpen access

  High-Fidelity 3D scene reconstruction plays a crucial role in autonomous driving by enabling novel data generation from existing datasets. This allows simulating safety-critical scenarios and augmenting training datasets without incurring further data collection costs. While recent advances in radiance fields have demonstrated promising results in 3D reconstruction and sensor data synthesis using cameras and LiDAR, their potential for radar remains largely unexplored. Radar is crucial for autonomous driving due to its robustness in adverse weather conditions like rain, fog, and snow, where optical sensors often struggle. Although the state-of-the-art radar-based neural representation shows promise for 3D driving scene reconstruction, it performs poorly in scenarios with significant radar noise, including receiver saturation and multipath reflection. Moreover, it is limited to synthesizing preprocessed, noise-excluded radar images, failing to address realistic radar data synthesis. To address these limitations, this paper proposes RadarSplat, which integrates Gaussian Splatting with novel radar noise modeling to enable realistic radar data synthesis and enhanced 3D reconstruction. Compared to the state-of-the-art, RadarSplat achieves superior radar image synthesis (+3.4 PSNR / 2.6x SSIM) and improved geometric reconstruction (-40% RMSE / 1.5x Accuracy), demonstrating its effectiveness in generating high-fidelity radar data and scene reconstruction. A project page is available at https://umautobots.github.io/radarsplat.

  PublisherOA PDFDOI

• Can Not Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty

  IEEE Transactions on Robotics · 2025-01-01 · 5 citations

  articleSenior author

  Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This paper proposes Autonomous Robust Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for robotic manipulators to address these challenges. ARMOUR first constructs a robust controller that tracks desired trajectories with bounded error despite uncertain dynamics. ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs required to track any trajectory within a continuum of desired trajectories. Finally, ARMOUR over-approximates the swept volume of the manipulator; this enables one to formulate an optimization problem that can be solved in real-time to synthesize provably-safe motions. This paper compares ARMOUR to state of the art methods on a set of challenging manipulation examples in simulation and demonstrates its ability to ensure safety on real hardware in the presence of model uncertainty without sacrificing performance. Project page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://roahmlab.github.io/armour/</uri>.

  PublisherDOI

• SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework

  ArXiv.org · 2025-12-15

  preprintOpen access

  This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures from the computer graphics community, such as OpenVDB, have demonstrated significantly improved computational and memory efficiency in volumetric scene representation. Although OpenVDB has been used for geometric mapping in robotics applications, semantic mapping for scene understanding with OpenVDB remains unexplored. In addition, existing semantic mapping systems lack support for integrating both fixed-category and open-language label predictions within a single framework. In this paper, we propose a novel 3D semantic mapping system that leverages the OpenVDB data structure and integrates a unified Bayesian update framework for both closed- and open-set semantic fusion. Our proposed framework, SLIM-VDB, achieves significant reduction in both memory and integration times compared to current state-of-the-art semantic mapping approaches, while maintaining comparable mapping accuracy. An open-source C++ codebase with a Python interface is available at https://github.com/umfieldrobotics/slim-vdb.

  PublisherOA PDFDOI

• An Invariant Preserving Sparse Spectral Discretization of the Continuum Equation

  SIAM Journal on Applied Dynamical Systems · 2025-09-11

  articleSenior author
  PublisherDOI

• These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models

  ArXiv.org · 2025-03-18

  preprintOpen accessSenior author

  This paper introduces a novel approach to uncertainty quantification for radiance fields by leveraging higher-order moments of the rendering equation. Uncertainty quantification is crucial for downstream tasks including view planning and scene understanding, where safety and robustness are paramount. However, the high dimensionality and complexity of radiance fields pose significant challenges for uncertainty quantification, limiting the use of these uncertainty quantification methods in high-speed decision-making. We demonstrate that the probabilistic nature of the rendering process enables efficient and differentiable computation of higher-order moments for radiance field outputs, including color, depth, and semantic predictions. Our method outperforms existing radiance field uncertainty estimation techniques while offering a more direct, computationally efficient, and differentiable formulation without the need for post-processing. Beyond uncertainty quantification, we also illustrate the utility of our approach in downstream applications such as next-best-view (NBV) selection and active ray sampling for neural radiance field training. Extensive experiments on synthetic and real-world scenes confirm the efficacy of our approach, which achieves state-of-the-art performance while maintaining simplicity.

  PublisherOA PDFDOI

• Phasing Through the Flames: Rapid Motion Planning with the AGHF PDE for Arbitrary Objective Functions and Constraints

  ArXiv.org · 2025-05-02

  preprintOpen accessSenior author

  The generation of optimal trajectories for high-dimensional robotic systems under constraints remains computationally challenging due to the need to simultaneously satisfy dynamic feasibility, input limits, and task-specific objectives while searching over high-dimensional spaces. Recent approaches using the Affine Geometric Heat Flow (AGHF) Partial Differential Equation (PDE) have demonstrated promising results, generating dynamically feasible trajectories for complex systems like the Digit V3 humanoid within seconds. These methods efficiently solve trajectory optimization problems over a two-dimensional domain by evolving an initial trajectory to minimize control effort. However, these AGHF approaches are limited to a single type of optimal control problem (i.e., minimizing the integral of squared control norms) and typically require initial guesses that satisfy constraints to ensure satisfactory convergence. These limitations restrict the potential utility of the AGHF PDE especially when trying to synthesize trajectories for robotic systems. This paper generalizes the AGHF formulation to accommodate arbitrary cost functions, significantly expanding the classes of trajectories that can be generated. This work also introduces a Phase1 - Phase 2 Algorithm that enables the use of constraint-violating initial guesses while guaranteeing satisfactory convergence. The effectiveness of the proposed method is demonstrated through comparative evaluations against state-of-the-art techniques across various dynamical systems and challenging trajectory generation problems. Project Page: https://roahmlab.github.io/BLAZE/

  PublisherOA PDFDOI

• DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time

  ArXiv.org · 2025-02-20

  preprintOpen accessSenior author

  Autonomous wire harness assembly requires robots to manipulate complex branched cables with high precision and reliability. A key challenge in automating this process is predicting how these flexible and branched structures behave under manipulation. Without accurate predictions, it is difficult for robots to reliably plan or execute assembly operations. While existing research has made progress in modeling single-threaded Deformable Linear Objects (DLOs), extending these approaches to Branched Deformable Linear Objects (BDLOs) presents fundamental challenges. The junction points in BDLOs create complex force interactions and strain propagation patterns that cannot be adequately captured by simply connecting multiple single-DLO models. To address these challenges, this paper presents Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT), a novel framework that combines a differentiable physics-based model with a learning framework to: 1) accurately model BDLO dynamics, including dynamic propagation at junction points and grasping in the middle of a BDLO, 2) achieve efficient computation for real-time inference, and 3) enable planning to demonstrate dexterous BDLO manipulation. A comprehensive series of real-world experiments demonstrates DEFT's efficacy in terms of accuracy, computational speed, and generalizability compared to state-of-the-art alternatives. Project page:https://roahmlab.github.io/DEFT/.

  PublisherOA PDFDOI

• Provably-Safe, Online System Identification

  2025-06-21 · 1 citations

  articleOpen accessSenior author

  Precise manipulation tasks require accurate knowledge of payload inertial parameters.Unfortunately, identifying these parameters for unknown payloads while ensuring that the robotic system satisfies its input and state constraints while avoiding collisions with the environment remains a significant challenge.This paper presents an integrated framework that enables robotic manipulators to safely and automatically identify payload parameters while maintaining operational safety guarantees.The framework consists of two synergistic components: an online trajectory planning and control framework that generates provablysafe exciting trajectories for system identification that can be tracked while respecting robot constraints and avoiding obstacles and a robust system identification method that computes rigorous overapproximative bounds on end-effector inertial parameters assuming bounded sensor noise.Experimental validation on a robotic manipulator performing challenging tasks with various unknown payloads demonstrates the framework's effectiveness in establishing accurate parameter bounds while maintaining safety throughout the identification process.

  PublisherDOI

Recent grants

• CAREER: Automating the Construction and Analysis of Models of Motion for Prehabilitative Care

  NSF · $500k · 2018–2024

• Designing Certified Controllers to Prevent Falls for Legged Robots

  NSF · $375k · 2016–2020

Frequent coauthors

• Matthew Johnson‐Roberson

  93 shared

• Shreyas Kousik

  33 shared

• Patrick Holmes

  University of Michigan–Ann Arbor

  27 shared

• Anil Aswani

  23 shared

• Pedro Hespanhol

  22 shared

• Daniel Bruder

  University of Michigan–Ann Arbor

  21 shared

• Jinsun Liu

  18 shared

• Katherine A. Skinner

  18 shared

Education

• PhD, Electrical Engineering and Computer Sciences

  University of California Berkeley

  2012

Awards & honors

• Seven ME Faculty receive 2020-21 College of Engineering Awar…
• Five ME Students Receive 2018 NSF GRFP awards
• Dasgupta and Vasudevan receive NSF CAREER Awards (2018)
• Vasudevan receives ONR 2018 Young Investigator Award