13 accepted papers
We present Actron3D, a framework that enables robots to acquire transferable 6-DoF manipulation skills from monocular, uncalibrated, RGB-only human demonstration videos. Our key idea is to represent manipulation knowledge within a video as a continuous neural function over object space. At the core …
Tuning Model Predictive Control (MPC) cost weights for multiple, competing objectives is labor-intensive. Derivative-free automated methods, such as Bayesian Optimization, reduce manual effort but remain slow, while Differentiable MPC (Diff-MPC) exploits solver sensitivities for faster gradient-base
Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstrations of automated drifting have been limited to research platforms with instantaneous torque delivery and independently actuated wheels, leaving t…
Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Model (VLM)-based methods primarily ground agents in static images and provide qualitative judgments, lacking the spatio–tempo…
This research introduces two efficient methods to estimate the collision risk of planned trajectories in autonomous driving under uncertain driving conditions. Deterministic collision checks of planned trajectories are often inaccurate or overly conservative, as noisy perception, localization errors…
Most autonomous cars rely on the availability of high-definition (HD) maps. Current research aims to address this constraint by directly predicting HD map elements from onboard sensors and reasoning about the relationships between the predicted map and traffic elements. Despite recent advancements,
We present a novel autonomous driving framework, DualAD, designed to imitate human reasoning during driving. DualAD comprises two layers: a rule-based motion planner at the bottom layer that handles routine driving tasks requiring minimal reasoning, and an upper layer featuring a rule-based text enc
Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates
Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and control. Traditional motion planning methods often struggle with the high instability and unpredictability of drifting, par
Propagating uncertainties through nonlinear system dynamics in the context of Stochastic Nonlinear Model Predictive Control (SNMPC) is challenging, especially for high-dimensional systems requiring real-time control and operating under time-variant uncertainties such as autonomous vehicles. In this…
Emergent-scene safety is the key milestone for fully autonomous driving, and reliable on-time prediction is essential to maintain safety in emergency scenarios. However, these emergency scenarios are long-tailed and hard to collect, which restricts the system from getting reliable predictions. In th…
This work aims to present a three-dimensional vehicle dynamics state estimation under varying signal quality. Few researchers have investigated the impact of three-dimensional road geometries on the state estimation and, thus, neglect road inclination and banking. Especially considering high velocit…
Robot localization is an inverse problem of finding a robot's pose using a map and sensor measurements. In recent years, Invertible Neural Networks (INN s) have successfully solved ambiguous inverse problems in various fields. This paper proposes a framework that approaches the localization problem…