Raffaello D'Andrea — accepted AI-conference papers

2026

KlaskTron: An Open-Source Platform for Physical Adversarial Multi-Agent RL

Aswin Karthik Ramachandran Venkatapathy, Jona Schulz, Maurus Derungs, Carlo Angelini, Tobias Meier, Raffaello D'andrea

RSS 2026poster

Progress in robot learning, particularly physical multi-agent reinforcement learning (MARL), is currently challenged by a limited availability of accessible, standardized benchmarks. While simulation-based MARL has produced remarkable emergent behaviors from coordinated team play to complex tool use…

Cited by 0 Source ↗Scholar ↗

2026

Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers

Panagiotis D. Grontas, Antonio Terpin, Efe C. Balta, Raffaello D'Andrea, John Lygeros

ICLR 2026oral

We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, $\Pi$net, leverages operator splitting for rapid and reliable projections in the forward pass, and the implicit function theorem for backpropagation. We deploy $\Pi$net as a feasible-by-de…

Cited by 0 Source ↗code ↗Scholar ↗

2025

Mastering the Labyrinth Game: Efficient Multimodal Reinforcement Learning with Selective Reconstruction

Thomas Bi, Ethan Marot, Aswin Ramachandran, Raffaello D'Andrea

IROS 2025

In previous work, model-based reinforcement learning was applied to a real-world labyrinth game to demonstrate sample-efficient learning using world models. In this paper, we further enhance sample efficiency and autonomy by introducing selective reconstruction: instead of reconstructing the full vi

Cited by 0 Source ↗Scholar ↗

2022

Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor

Pietro Griffa, Carmelo Sferrazza, Raffaello D'Andrea

ICRA 2022poster

Grasping objects whose physical properties are unknown is still a great challenge in robotics. Most solutions rely entirely on visual data to plan the best grasping strategy. However, to match human abilities and be able to reliably pick and hold unknown objects, the integration of an artificial sen…

Cited by 17 Source ↗Scholar ↗

2021

Zero-Shot Sim-to-Real Transfer of Tactile Control Policies for Aggressive Swing-Up Manipulation

Thomas Bi, Carmelo Sferrazza, Raffaello D'Andrea

RA-L 2021

This letter aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated. For this purpose, a robotic system is presented that is able to swing up poles of different

Cited by 39 Source ↗Scholar ↗

2019

A Method for Reducing the Complexity of Model Predictive Control in Robotics Applications

Michael Muehlebach, Raffaello D'Andrea

RA-L 2019

This letter describes an approach for parametrizing input and state trajectories in model predictive control. The parametrization is designed to be invariant to time shifts, which enables warm-starting the successive optimization problems and reduces the computational complexity of the online optimi

Cited by 9 Source ↗Scholar ↗

2018

Design, Modeling and Control of a Soft Robotic Arm

Matthias Hofer, Raffaello D'Andrea

IROS 2018poster

In this paper we present the design of a hybrid robotic arm using soft, inflatable bladders for actuation. Low cost switching valves are used for pressure control, where the valve model is identified experimentally. A model of the robotic arm is derived based on system identification and used to der…

Cited by 46 Source ↗Scholar ↗

2018

State Estimate Recovery for Autonomous Quadcopters

Luciano Beffa, Anton Ledergerber, Raffaello D'Andrea

IROS 2018poster

A method for recovery from the complete loss of the state estimate is presented for autonomous quadcopters. Given an aerodynamic force model, the only measurements used to reinitialize the state estimate by means of a bank of extended Kalman filters are the angular rate and linear acceleration measu…

Cited by 3 Source ↗Scholar ↗

2017

A global controller for flying wing tailsitter vehicles

Robin Ritz, Raffaello D'Andrea

ICRA 2017poster

We present a global controller for tracking nominal trajectories with a flying wing tailsitter vehicle. The control strategy is based on a first-principles model of the vehicle dynamics that captures all relevant aerodynamic effects, and we apply an onboard parameter learning scheme in order to esti…

Cited by 73 Source ↗Scholar ↗

2017

Implementation of a parametrized infinite-horizon model predictive control scheme with stability guarantees

Michael Muehlebach, Carmelo Sferrazza, Raffaello D'Andrea

ICRA 2017poster

This article discusses the implementation of an infinite-horizon model predictive control approach that is based on representing input and state trajectories by a linear combination of basis functions. An iterative constraint sampling strategy is presented for guaranteeing constraint satisfaction ov…

Cited by 10 Source ↗Scholar ↗

2016

A controllable flying vehicle with a single moving part

Weixuan Zhang, Mark W. Müller, Raffaello D'Andrea

ICRA 2016

This paper presents the "monospinner": a mechanically simple flying vehicle with only one moving part. The vehicle is shown to be controllable in three translational degrees of freedom and two rotational degrees of freedom. The vehicle has a single scalar control input, the thrust magnitude, and is

Cited by 35 Source ↗Scholar ↗

2016

Application of an approximate model predictive control scheme on an unmanned aerial vehicle

Matthias Hofer, Michael Muehlebach, Raffaello D'Andrea

ICRA 2016

An approximate model predictive control approach is applied on an unmanned aerial vehicle with limited computational resources. A novel method using a continuous time parametrization of the state and input trajectory is used to derive a compact description of the optimal control problem. Different f

Cited by 33 Source ↗Scholar ↗

2016

Design, modeling and control of an omni-directional aerial vehicle

Dario Brescianini, Raffaello D'Andrea

ICRA 2016

In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design

Cited by 241 Source ↗Scholar ↗

2015

A robot self-localization system using one-way ultra-wideband communication

Anton Ledergerber, Michael Hamer, Raffaello D'Andrea

IROS 2015poster

A robot localization system is presented that enables a robot to estimate its position within some space by passively receiving ultra-wideband radio signals from fixed-position modules. Communication from the fixed-position modules is one-way, allowing the system to scale to multiple robots. Further…

Cited by 191 Source ↗Scholar ↗

2015

Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation

Mark W. Mueller, Michael Hamer, Raffaello D'Andrea

ICRA 2015poster

A state estimator for a quadrocopter is presented, using measurements from an accelerometer, angular rate gyroscope, and a set of ultra-wideband ranging radios. The estimator uses an extended aerodynamic model for the quadrocopter, where the full 3D airspeed is observable through accelerometer measu…

Cited by 260 Source ↗Scholar ↗

2015

High-speed, steady flight with a quadrocopter in a confined environment using a tether

Maximilian Schulz, Federico Augugliaro, Robin Ritz, Raffaello D'Andrea

IROS 2015poster

This paper presents a method that enables highspeed, steady flight in confined spaces for tethered quadrocopters. Thanks to the centripetal force exerted by the tether, high-speed trajectories along circles at different velocities, accelerations, and orientations in space can be flown. Various circu…

Cited by 32 Source ↗Scholar ↗

2015

Knot-tying with flying machines for aerial construction

Federico Augugliaro, Emanuele Zarfati, Ammar Mirjan, Raffaello D'Andrea

IROS 2015poster

This paper addresses one of the fundamental tasks for the aerial assembly of tensile structures: aerial knot-tying. It presents a framework for representing and realizing knots with flying machines. A suitable representation of the knot topology is introduced taking into account the use of supportin…

Cited by 38 Source ↗Scholar ↗

2015

Rendezvous with bearing-only information and limited sensing range

Maximilian Kriegleder, Sundara Tejaswi Digumarti, Raymond Oung, Raffaello D'Andrea

ICRA 2015poster

This paper proposes a generalized algorithm that enables mobile agents to meet in a bounded region based only on bearing information of other agents within their vicinity. Each agent repeatedly employs a stop-and-go strategy consisting of the following three actions: (1) Estimate the bearing of agen…

Cited by 10 Source ↗Scholar ↗