22 citations · 34 across the 23 of their papers we have counts for
25 papers
Learning Continuous Neural Representation of Stochastic Hybrid Systems
Sangli Teng, Hang Liu, Koushil Sreenath
A stochastic hybrid system (SHS) is governed by a stochastic differential equation (SDE) describing the continuous dynamics and a Markov reset kernel triggered on the guard surface…
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Dongxiu Liu, Haoyi Niu, Peng Cheng +5
In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time…
Embedding Hybrid Systems into Continuous Latent Vector Fields
Sangli Teng, Hang Liu, Koushil Sreenath
This work proves that an -dimensional hybrid system can be embedded into an -dimensional Euclidean space equipped with a continuous vector field on its embedded image wheneve…
LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies
Sangli Teng, Ruiqi Zhang, Tzu-Yuan Lin +5
Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. While direct methods are widely used, the existing constrained optimizers typical…
A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
I-Chia Chang, Xinyan Huang, Tzu-Yuan Lin +5
Legged robots have demonstrated remarkable agility on rigid, stationary ground, but their locomotion reliability remains limited in non-inertial environments, where the supporting…
Stein Variational Ergodic Surface Coverage with SE(3) Constraints
Jiayun Li, Yufeng Jin, Sangli Teng +2
Surface manipulation tasks require robots to generate trajectories that comprehensively cover complex 3D surfaces while maintaining precise end-effector poses. Existing ergodic tra…
MePoly: Max Entropy Polynomial Policy Optimization
Hang Liu, Sangli Teng, Maani Ghaffari
Stochastic Optimal Control provides a unified mathematical framework for solving complex decision-making problems, encompassing paradigms such as maximum entropy reinforcement lear…
CHyLL: Learning Continuous Neural Representations of Hybrid Systems
Sangli Teng, Hang Liu, Jingyu Song +1
Learning the flows of hybrid systems that have both continuous and discrete time dynamics is challenging. The existing method learns the dynamics in each discrete mode, which suffe…
Robust Trajectory Tracking of Autonomous Surface Vehicle via Lie Algebraic Online MPC
Yinan Dong, Ziyu Xu, Tsimafei Lazouski +2
Autonomous surface vehicles (ASVs) are influenced by environmental disturbances such as wind and waves, making accurate trajectory tracking a persistent challenge in dynamic marine…
Ego-Vision World Model for Humanoid Contact Planning
Hang Liu, Yuman Gao, Sangli Teng +5
Enabling humanoid robots to exploit physical contact, rather than simply avoid collisions, is crucial for autonomy in unstructured environments. Traditional optimization-based plan…
Learning Hybrid Dynamics via Convex Optimizations
Kaito Iwasaki, Sangli Teng, Anthony Bloch +1
This paper investigates the problem of identifying state-dependent switching systems, a class of hybrid dynamical systems that combine multiple linear or nonlinear modes. We propos…
Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
Sangli Teng, Harry Zhang, David Jin +4
Designing optimal Bayes filters for nonlinear non-Gaussian systems is a challenging task. The main difficulties are: 1) representing complex beliefs, 2) handling non-Gaussian noise…
Riemannian Direct Trajectory Optimization of Rigid Bodies on Matrix Lie Groups
Sangli Teng, Tzu-Yuan Lin, William A Clark +2
Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. Although direct trajectory optimization is widely applied to solve this problem,…
Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding
Hang Liu, Sangli Teng, Ben Liu +2
Hybrid dynamical systems, which include continuous flow and discrete mode switching, can model robotics tasks like legged robot locomotion. Model-based methods usually depend on pr…
A Generalized Metriplectic System via Free Energy and System~Identification via Bilevel Convex Optimization
Sangli Teng, Kaito Iwasaki, William Clark +4
This work generalizes the classical metriplectic formalism to model Hamiltonian systems with nonconservative dissipation. Classical metriplectic representations allow for the descr…
Legged Robot State Estimation within Non-inertial Environments
Zijian He, Sangli Teng, Tzu-Yuan Lin +2
This paper investigates the robot state estimation problem within a non-inertial environment. The proposed state estimation approach relaxes the common assumption of static ground…
GMKF: Generalized Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
Sangli Teng, Harry Zhang, David Jin +3
This paper develops a new filtering approach for state estimation in polynomial systems corrupted by arbitrary noise, which commonly arise in robotics. We first consider a batch se…
Convex Geometric Motion Planning on Lie Groups via Moment Relaxation
Sangli Teng, Ashkan Jasour, Ram Vasudevan +1
This paper reports a novel result: with proper robot models on matrix Lie groups, one can formulate the kinodynamic motion planning problem for rigid body systems as \emph{exact} p…
Convex Geometric Trajectory Tracking using Lie Algebraic MPC for Autonomous Marine Vehicles
Junwoo Jang, Sangli Teng, Maani Ghaffari
Controlling marine vehicles in challenging environments is a complex task due to the presence of nonlinear hydrodynamics and uncertain external disturbances. Despite nonlinear mode…
Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer
Xihang Yu, Sangli Teng, Theodor Chakhachiro +7
This paper develops a novel slip estimator using the invariant observer design theory and Disturbance Observer (DOB). The proposed state estimator for mobile robots is fully propri…
Lie Algebraic Cost Function Design for Control on Lie Groups
Sangli Teng, William Clark, Anthony Bloch +2
This paper presents a control framework on Lie groups by designing the control objective in its Lie algebra. Control on Lie groups is challenging due to its nonlinear nature and di…
Input Influence Matrix Design for MIMO Discrete-Time Ultra-Local Model
Sangli Teng, Amit K. Sanyal, Ram Vasudevan +2
Ultra-Local Models (ULM) have been applied to perform model-free control of nonlinear systems with unknown or partially known dynamics. Unfortunately, extending these methods to MI…
An Error-State Model Predictive Control on Connected Matrix Lie Groups for Legged Robot Control
Sangli Teng, Dianhao Chen, William Clark +1
This paper reports on a new error-state Model Predictive Control (MPC) approach to connected matrix Lie groups for robot control. The linearized tracking error dynamics and the lin…
Legged Robot State Estimation in Slippery Environments Using Invariant Extended Kalman Filter with Velocity Update
Sangli Teng, Mark Wilfried Mueller, Koushil Sreenath
This paper proposes a state estimator for legged robots operating in slippery environments. An Invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity…