44 citations · 59 across the 27 of their papers we have counts for
33 papers
GGraph: A GPU-Accelerated Library for Graph-Based Robot Deployment and Learning
Yash Rampuria, Rohan Garg, Daniele Gammelli +1
Autonomous mobile robots require spatial representations that capture geometry and semantics while staying sparse enough for planning in large, unknown environments. Graphs provide…
PLUTO: An Agentic AI Tool for Interactive Spacecraft Rendezvous Trajectory Design
Eleanor Brosius, Yuji Takubo, Daniele Gammelli +2
Modern space missions require trajectory design methods capable of efficiently adapting to diverse mission objectives and operational constraints. Despite the maturity of non-conve…
Filter-Aware Fine-Tuning for Safe Humanoid Whole-Body Tracking
Pranit Mohnot, Christian Helten, Daniele Gammelli +1
Safe whole-body motion is essential for deploying humanoid robots in unstructured environments. Modern humanoid control commonly separates reference specification from execution, w…
OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous
Yuji Takubo, Daniele Gammelli, Marco Pavone +1
Spacecraft rendezvous and proximity operations (RPO) are currently planned through an expertise-intensive process in which engineers translate high-level operational intent into sa…
Principles of Robot Autonomy
Daniele Gammelli, Joseph Lorenzetti, Katie Luo +2
Autonomous robots are moving rapidly from research labs into everyday life - on roads, in the air, in warehouses, and in space. Robot autonomy is no longer solely an academic pursu…
Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning
Matthew Foutter, Matteo Cercola, Lena Wild +4
Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models. However, whet…
Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models
Eleanor Brosius, Yuji Takubo, Daniele Gammelli +2
Trajectory optimization is a critical component for enabling safe and reliable autonomous operations in space exploration. As space missions increase in frequency, complexity, and…
Graph Neural Model Predictive Control for High-Dimensional Systems
Patrick Benito Eberhard, Luis Pabon, Daniele Gammelli +5
The control of high-dimensional systems, such as soft robots, requires models that faithfully capture complex dynamics while remaining computationally tractable. This work presents…
Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systems
Emil Kragh Toft, Carolin Schmidt, Daniele Gammelli +1
Autonomous Mobility-on-Demand (AMoD) systems promise to revolutionize urban transportation by providing affordable on-demand services to meet growing travel demand. However, realis…
Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous
Yuji Takubo, Arpit Dwivedi, Sukeerth Ramkumar +4
Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autono…
GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding
Johannes Gaber, Meshal Alharbi, Daniele Gammelli +1
Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task s…
Multi-Timescale Model Predictive Control for Slow-Fast Systems
Lukas Schroth, Daniel Morton, Amon Lahr +3
Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse applications. While model fidelity is cru…
Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
Yuji Takubo, Daniele Gammelli, Marco Pavone +1
Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid ex…
Reproducibility in the Control of Autonomous Mobility-on-Demand Systems
Xinling Li, Meshal Alharbi, Daniele Gammelli +7
Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD…
Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
Oskar Bohn Lassen, Serio Agriesti, Mohamed Eldafrawi +4
The Traffic Assignment Problem is a fundamental, yet computationally expensive, task in transportation modeling, especially for large-scale networks. Traditional methods require it…
Vision Foundation Model Embedding-Based Semantic Anomaly Detection
Max Peter Ronecker, Matthew Foutter, Amine Elhafsi +4
Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonom…
Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling
Xinling Li, Daniele Gammelli, Alex Wallar +2
Rapid urbanization has led to a surge of customizable mobility demand in urban areas, which makes on-demand services increasingly popular. On-demand services are flexible while red…
Robo-taxi Fleet Coordination at Scale via Reinforcement Learning
Luigi Tresca, Carolin Schmidt, James Harrison +4
Fleets of robo-taxis offering on-demand transportation services, commonly known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise for societal benefits, suc…
Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling
Davide Celestini, Daniele Gammelli, Tommaso Guffanti +3
Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. H…
Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers
Davide Celestini, Amirhossein Afsharrad, Daniele Gammelli +6
Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for so…
Offline Hierarchical Reinforcement Learning via Inverse Optimization
Carolin Schmidt, Daniele Gammelli, James Harrison +2
Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon plannin…
Towards Robust Spacecraft Trajectory Optimization via Transformers
Yuji Takubo, Tommaso Guffanti, Daniele Gammelli +2
Future multi-spacecraft missions require robust autonomous trajectory optimization capabilities to ensure safe and efficient rendezvous operations. This capability hinges on solvin…
Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications
Matthew Foutter, Daniele Gammelli, Justin Kruger +5
Foundation Models (FMs), e.g., large language models, possess attributes of intelligence which offer promise to endow a robot with the contextual understanding necessary to navigat…
Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning
Aaryan Singhal, Daniele Gammelli, Justin Luke +3
Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available vehicles to ride requests, rebalancing idle…