17 citations · 23 across the 29 of their papers we have counts for
29 papers
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
Yuan Gao, Sebastian Müller, Mattia Piccinini +5
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains…
Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann +6
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-depen…
Control-Informed Constraint Adaptation in Minimum-Time Trajectory Planning for Autonomous Racing
Ann-Kathrin Schwehn, Alexander Langmann, Mattia Piccinini +1
Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume p…
SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing
Zhouheng Li, Fangguo Zhao, Mattia Piccinini +6
Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversit…
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
Yuan Gao, Wenting Miao, Mattia Piccinini +3
Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatic…
A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing
Alexander Langmann, Frederico Pita de Araujo, Mattia Piccinini +1
Autonomous racing demands planning algorithms that balance vehicle dynamics at the limits of handling with strategic decision-making in competitive multi-agent scenarios. Game theo…
Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators
Christian Oefinger, Finn Rasmus Schäfer, Korbinian Moller +2
Across robotics, World Models (WMs) are increasingly used to evaluate action policies by simulating the consequences of actions in an imagined world, and returning a success or saf…
Unified Video-Action Joint Denoising for Dexterous Action and Data Generation
Dingrui Wang, YuAn Wang, Jinkun Liu +4
Recent world action models leverage video foundation models by aligning broad visual-dynamics priors with executable robot actions. We revisit this alignment from a distributional…
Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
Georg Jank, Mattia Piccinini, Sebastian Wenk +3
Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections from the feedback controller…
How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study
Roberto Brusnicki, Mattia Piccinini, Johannes Betz
Vision-Language Models (VLMs) are increasingly proposed for autonomous driving tasks, yet their performance on sequential driving scenes remains poorly characterized, particularly…
Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform
Mattia Piccinini, Patrick Zambiasi, Aniello Mungiello +3
We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autonomous driving to the limits.…
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Finn Rasmus Schäfer, Yuan Gao, Dingrui Wang +5
While Vision-Language Models (VLMs) have advanced high-level reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains…
StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
Yuan Gao, Dengyuan Hua, Mattia Piccinini +4
Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which tra…
Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?
Dingrui Wang, Zhihao Liang, Hongyuan Ye +13
While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target…
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
Alexander Langmann, Yevhenii Tokarev, Mattia Piccinini +2
Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, t…
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
Roberto Brusnicki, David Pop, Yuan Gao +2
Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution semantic anomalies. While VLMs have emerged as promising tools for perception,…
Enhancing Physical Consistency in Lightweight World Models
Dingrui Wang, Zhexiao Sun, Zhouheng Li +8
A major challenge in deploying world models is the trade-off between size and performance. Large world models can capture rich physical dynamics but require massive computing resou…
Real-time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints
Mattia Piazza, Mattia Piccinini, Sebastiano Taddei +2
The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with parti…
NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving
Yuan Gao, Mattia Piccinini, Roberto Brusnicki +2
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…
Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini +2
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uni…
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
Mattia Piccinini, Aniello Mungiello, Georg Jank +3
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving…
MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
Marc Kaufeld, Mattia Piccinini, Johannes Betz
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for…
Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
Bei Zhou, Baha Zarrouki, Mattia Piccinini +3
Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and con…