74 citations · 227 across the 87 of their papers we have counts for
20 papers · 1 filter
Lies We Can Trust: Quantifying Action Uncertainty with Inaccurate Stochastic Dynamics through Conformalized Nonholonomic Lie groups
Luís Marques, Maani Ghaffari, Dmitry Berenson
We propose Conformal Lie-group Action Prediction Sets (CLAPS), a symmetry-aware conformal prediction-based algorithm that constructs, for a given action, a set guaranteed to contai…
Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints
Minghan Zhu, Zhiyi Wang, Qihang Sun +2
Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because camera observations are partial due to occlusions. The scene may…
Topological Dynamics via Learned Hybrid Systems
Bernardo Rivas, Kaito Iwasaki, William Kalies +2
The analysis of global dynamics, particularly the identification and characterization of attractors and their regions of attraction, is essential for complex nonlinear and hybrid s…
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…
Switching Network System Identification via Convex Optimizations
Kaito Iwasaki, Anthony Bloch, Maani Ghaffari
This paper introduces a convex optimization framework for identifying switched network systems, in which both the node dynamics and the underlying graph topology switch between a f…
Equivariant Neural Networks for General Linear Symmetries on Lie Algebras
Chankyo Kim, Sicheng Zhao, Minghan Zhu +2
Many scientific and geometric problems exhibit general linear symmetries, yet most equivariant neural networks are built for compact groups or simple vector features, limiting thei…
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…
Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
Zeyu Han, Shuocheng Yang, Minghan Zhu +4
Autonomous vehicles and robots rely on accurate odometry estimation in GPS-denied environments. While LiDARs and cameras struggle under extreme weather, 4D mmWave radar emerges as…
Super LiDAR Intensity for Robotic Perception
Wei Gao, Jie Zhang, Mingle Zhao +6
Conventionally, human intuition defines vision as a modality of passive optical sensing, relying on ambient light to perceive the environment. However, active optical sensing, whic…
Imaginative World Modeling with Scene Graphs for Embodied Agent Navigation
Yue Hu, Junzhe Wu, Ruihan Xu +5
Semantic navigation requires an agent to navigate toward a specified target in an unseen environment. Employing an imaginative navigation strategy that predicts future scenes befor…
Registration beyond Points: General Affine Subspace Alignment via Geodesic Distance on Grassmann Manifold
Jaeho Shin, Hyeonjae Gil, Junwoo Jang +2
Affine Grassmannian has been favored for expressing proximity between lines and planes due to its theoretical exactness in measuring distances among features. Despite this advantag…
GPS-DRIFT: Marine Surface Robot Localization using IMU-GPS Fusion and Invariant Filtering
Surya Pratap Singh, Tsimafei Lazouski, Maani Ghaffari
This paper presents an extension of the DRIFT invariant state estimation framework, enabling robust fusion of GPS and IMU data for accurate pose and heading estimation. Originally…
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,…
Debiasing 6-DOF IMU via Hierarchical Learning of Continuous Bias Dynamics
Ben Liu, Tzu-Yuan Lin, Wei Zhang +1
This paper develops a deep learning approach to the online debiasing of IMU gyroscopes and accelerometers. Most existing methods rely on implicitly learning a bias term to compensa…
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…
POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality
Joey Wilson, Marcelino Almeida, Sachit Mahajan +6
In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to…
Learning Orientation Field for OSM-Guided Autonomous Navigation
Yuming Huang, Wei Gao, Zhiyuan Zhang +4
OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, ex…
Learning Implicit Social Navigation Behavior using Deep Inverse Reinforcement Learning
Tribhi Kathuria, Ke Liu, Junwoo Jang +2
This paper reports on learning a reward map for social navigation in dynamic environments where the robot can reason about its path at any time, given agents' trajectories and scen…