27 citations · 93 across the 21 of their papers we have counts for
24 papers
LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation
Nicolas Baumann, Liam Boyle, Pu Deng +5
Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context, serving as a catalyst for open-voca…
MCTR: Midpoint Corrected Triangulation for Autonomous Racing via Digital Twin Simulation in CARLA
Junhao Ye, Cheng Hu, Yiqin Wang +6
In autonomous racing, reactive controllers eliminate the computational burden of the full See-Think-Act autonomy stack by directly mapping sensor inputs to control actions. This by…
M-Predictive Spliner: Enabling Spatiotemporal Multi-Opponent Overtaking for Autonomous Racing
Nadine Imholz, Maurice Brunner, Nicolas Baumann +2
Unrestricted multi-agent racing presents a significant research challenge, requiring decision-making at the limits of a robot's operational capabilities. While previous approaches…
R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
Maurice Brunner, Edoardo Ghignone, Nicolas Baumann +1
Autonomous racing has emerged as a crucial testbed for autonomous driving algorithms, necessitating a simulation environment for both vehicle dynamics and sensor behavior. Striking…
DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing
Luca Tognoni, Neil Reichlin, Edoardo Ghignone +4
Reactive controllers for autonomous racing avoid the computational overhead of full ee-Think-Act autonomy stacks by directly mapping sensor input to control actions, eliminating th…
Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow
Liam Boyle, Jonas Kühne, Nicolas Baumann +2
Accurate velocity estimation is critical in mobile robotics, particularly for driver assistance systems and autonomous driving. Wheel odometry fused with Inertial Measurement Unit…
Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing
Benedict Hildisch, Edoardo Ghignone, Nicolas Baumann +3
Autonomous racing presents unique challenges due to its non-linear dynamics, the high speed involved, and the critical need for real-time decision-making under dynamic and unpredic…
RobotxR1: Enabling Embodied Robotic Intelligence on Large Language Models through Closed-Loop Reinforcement Learning
Liam Boyle, Nicolas Baumann, Paviththiren Sivasothilingam +2
Future robotic systems operating in real-world environments will require on-board embodied intelligence without continuous cloud connection, balancing capabilities with constraints…
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models
Nicolas Baumann, Cheng Hu, Paviththiren Sivasothilingam +4
Neural Networks (NNs) trained through supervised learning struggle with managing edge-case scenarios common in real-world driving due to the intractability of exhaustive datasets c…
TinyCenterSpeed: Efficient Center-Based Object Detection for Autonomous Racing
Neil Reichlin, Nicolas Baumann, Edoardo Ghignone +1
Perception within autonomous driving is nearly synonymous with Neural Networks (NNs). Yet, the domain of autonomous racing is often characterized by scaled, computationally limited…
GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR
Yangyang Xie, Cheng Hu, Nicolas Baumann +3
Autonomous drifting is a complex challenge due to the highly nonlinear dynamics and the need for precise real-time control, especially in uncertain environments. To address these l…
FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions
Cheng Hu, Jihao Huang, Wule Mao +7
Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle's dynamics and ensure safety and real-time performance run…
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms
Edoardo Ghignone, Nicolas Baumann, Cheng Hu +4
Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tir…
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
Nicolas Baumann, Edoardo Ghignone, Cheng Hu +6
Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner that l…
Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
Onur Dikici, Edoardo Ghignone, Cheng Hu +5
Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as state-of-the-art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters…
DSORT-MCU: Detecting Small Objects in Real-Time on Microcontroller Units
Liam Boyle, Julian Moosmann, Nicolas Baumann +2
Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, th…
CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
Nicolas Baumann, Michael Baumgartner, Edoardo Ghignone +5
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for hi…
ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
Nicolas Baumann, Edoardo Ghignone, Jonas Kühne +11
Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their…
Robustness Evaluation of Localization Techniques for Autonomous Racing
Tian Yi Lim, Edoardo Ghignone, Nicolas Baumann +1
This work introduces SynPF, an MCL-based algorithm tailored for high-speed racing environments. Benchmarked against Cartographer, a state-of-the-art pose-graph SLAM algorithm, SynP…
Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications
Liam Boyle, Nicolas Baumann, Seonyeong Heo +1
Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, th…
Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study
Michael Loetscher, Nicolas Baumann, Edoardo Ghignone +2
Range-measuring sensors play a critical role in autonomous driving systems. While LiDAR technology has been dominant, its vulnerability to adverse weather conditions is well-docume…
Towards Robust Velocity and Position Estimation of Opponents for Autonomous Racing Using Low-Power Radar
Andrea Ronco, Nicolas Baumann, Marco Giordano +1
This paper presents the design and development of an intelligent subsystem that includes a novel low-power radar sensor integrated into an autonomous racing perception pipeline to…
Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing
Jonathan Becker, Nadine Imholz, Luca Schwarzenbach +3
Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving.…
TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach
Edoardo Ghignone, Nicolas Baumann, Mike Boss +1
Autonomous racing is becoming popular for academic and industry researchers as a test for general autonomous driving by pushing perception, planning, and control algorithms to thei…