Sumanth Tangirala

@conference{granados2025kinodynamic,
title = {Kinodynamic Trajectory Following with STELA: Simultaneous Trajectory Estimation & Local Adaptation},
author = {E Granados and S Tangirala and K Bekris},
url = {https://prx-kinodynamic.github.io/projects/stela

https://arxiv.org/abs/2504.20009},
year = {2025},
date = {2025-06-21},
booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
abstract = {State estimation and control are often addressed separately, leading to unsafe execution due to sensing noise, execution errors, and discrepancies between the planning model and reality. Simultaneous control and trajectory estimation using probabilistic graphical models has been proposed as a unified solution to these challenges. Previous work, however, relies heavily on appropriate Gaussian priors and is limited to holonomic robots with linear time-varying models. The current research extends graphical optimization methods to vehicles with arbitrary dynamical models via Simultaneous Trajectory Estimation and Local Adaptation (STELA). The overall approach initializes feasible trajectories using a kinodynamic, sampling-based motion planner. Then, it simultaneously: (i) estimates the past trajectory based on noisy observations, and (ii) adapts the controls to be executed to minimize deviations from the planned, feasible trajectory, while avoiding collisions. The proposed factor graph representation of trajectories in STELA can be applied for any dynamical system given access to first or second-order state update equations, and introduces the duration of execution between two states in the trajectory discretization as an optimization variable. These features provide both generalization and flexibility in trajectory following. In addition to targeting computational efficiency, the proposed strategy performs incremental updates of the factor graph using the iSAM algorithm and introduces a time-window mechanism. This mechanism allows the factor graph to be dynamically updated to operate over a limited history and forward horizon of the planned trajectory. This enables online updates of controls at a minimum of 10Hz. Experiments demonstrate that STELA achieves at least comparable performance to previous frameworks on idealized vehicles with linear dynamics. STELA also directly applies to and successfully solves trajectory following problems for more complex dynamical models. Beyond generalization, simulations assess STELA's robustness under varying levels of sensing and execution noise, while ablation studies highlight the importance of different components of STELA. Real-world experiments validate STELA's practical applicability on a low-cost MuSHR robot, which exhibits high noise and non-trivial dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{Vieira:2024aa,
title = {MORALS: Analysis of High-Dimensional Robot Controllers Via Topological Tools in a Latent Space},
author = {E Vieira and A Sivaramakrishnan and S Tangirala and E Granados and K Mischaikow and K Bekris},
url = {https://arxiv.org/abs/2310.03246},
year = {2024},
date = {2024-05-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Yokohama, Japan (Nominated for Best Paper Award in Automation)},
abstract = {Estimating the region of attraction (𝚁𝚘𝙰) for a robotic system's controller is essential for safe application and controller composition. Many existing methods require access to a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be data-hungry. In prior work, we have demonstrated that topological tools based on Morse Graphs offer data-efficient 𝚁𝚘𝙰 estimation without needing an analytical model. They struggle, however, with high-dimensional systems as they operate over a discretization of the state space. This paper presents Morse Graph-aided discovery of Regions of Attraction in a learned Latent Space (𝙼𝙾𝚁𝙰𝙻𝚂). The approach combines autoencoding neural networks with Morse Graphs. 𝙼𝙾𝚁𝙰𝙻𝚂 shows promising predictive capabilities in estimating attractors and their 𝚁𝚘𝙰s for data-driven controllers operating over high-dimensional systems, including a 67-dim humanoid robot and a 96-dim 3-fingered manipulator. It first projects the dynamics of the controlled system into a learned latent space. Then, it constructs a reduced form of Morse Graphs representing the bistability of the underlying dynamics, i.e., detecting when the controller results in a desired versus an undesired behavior. The evaluation on high-dimensional robotic datasets indicates the data efficiency of the approach in 𝚁𝚘𝙰 estimation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}