Ruggero Carli — accepted AI-conference papers

2026

Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd “AI Olympics with RealAIGym” Competition

Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera, Jean Seong Bjorn Choe, Bumkyu Choi, Tim Faust +15

ICRA 2026poster

In robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields to achieve reliable control in diverse tasks. In order to get a clear understanding of their individual strengths and weaknesses and …

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2025

PACE: Proactive Assistance in Human-Robot Collaboration Through Action-Completion Estimation

Davide De Lazzari, Matteo Terreran, Giulio Giacomuzzo, Siddarth Jain, Pietro Falco, Ruggero Carli +1

ICRA 2025

This paper introduces the Proactive Assistance through action-Completion Estimation (PACE) framework, designed to enhance human-robot collaboration through real-time monitoring of human progress. PACE incorporates a novel method that combines Dynamic Time Warping (DTW) with correlation analysis to t

Cited by 3 Source ↗Scholar ↗

2025

Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation With Large Language Models

Niccolò Turcato, Matteo Iovino, Aris I. Synodinos, Alberto Dalla Libera, Ruggero Carli, Pietro Falco

RA-L 2025

Recent advancements in Large Language Models (LLMs) and Visual Language Models (VLMs) have significantly impacted robotics, enabling high-level semantic motion planning applications. Reinforcement Learning (RL), a complementary paradigm, enables agents to autonomously optimize complex behaviors thro

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2024

Reinforcement Learning for Athletic Intelligence: Lessons from the 1st “AI Olympics with RealAIGym” Competition

Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera, Chi Zhang, Theo Vincent, Shubham Vyas +9

IJCAI 2024poster

As artificial intelligence gains new capabilities, it becomes important to evaluate it on real-world tasks. In particular, the fields of robotics and reinforcement learning (RL) are lacking in standardized benchmarking tasks on real hardware. To facilitate reproducibility and stimulate algorithmi…

Cited by 11 Source ↗PDF ↗Scholar ↗

2020

A Data-Efficient Geometrically Inspired Polynomial Kernel for Robot Inverse Dynamic

Alberto Dalla Libera, Ruggero Carli

RA-L 2020

In this letter, we introduce a novel data-driven inverse dynamics estimator based on Gaussian Process Regression. Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, we propose the use of a novel kernel, called Geometrically Inspired Poly

Cited by 21 Source ↗Scholar ↗

2019

Prediction-correction for Nonsmooth Time-varying Optimization via Forward-backward Envelopes

Nicola Bastianello, Andrea Simonetto, Ruggero Carli

ICASSP 2019accepted

We present an algorithm for minimizing the sum of a strongly convex time-varying function with a time-invariant, convex, and nonsmooth function. The proposed algorithm employs the prediction-correction scheme alongside the forward-backward envelope, and we are able to prove the convergence of the so…

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