Alberto Dalla Libera — 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

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

Cited by 3 Source ↗Scholar ↗

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 ↗

2020

Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements

Alberto Dalla Libera, Diego Romeres, Devesh K. Jha, Bill Yerazunis, Daniel Nikovski

RA-L 2020

In this letter, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR). In many mechanical systems, only positions can be measured by the sensing instruments. Then, instead of representing the system state as sugges

Cited by 13 Source ↗Scholar ↗