1 citations · 2 across the 7 of their papers we have counts for
9 papers
Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
Vittorio Giammarino, Anastasios Manganaris, Ahmed H. Qureshi
Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned reinforcement learning (GCR…
Formal Methods in Robot Policy Learning and Verification: A Survey on Current Techniques and Future Directions
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi +1
As hardware and software systems have grown in complexity, formal methods have been indispensable tools for rigorously specifying acceptable behaviors, synthesizing programs to mee…
Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning
Vittorio Giammarino, Ahmed H. Qureshi
Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In th…
Automaton Constrained Q-Learning
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigm…
Robust Point Cloud Reinforcement Learning via PCA-Based Canonicalization
Michael Bezick, Vittorio Giammarino, Ahmed H. Qureshi
Reinforcement Learning (RL) from raw visual input has achieved impressive successes in recent years, yet it remains fragile to out-of-distribution variations such as changes in lig…
Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning
Vittorio Giammarino, Ruiqi Ni, Ahmed H. Qureshi
Offline Goal-Conditioned Reinforcement Learning (GCRL) holds great promise for domains such as autonomous navigation and locomotion, where collecting interactive data is costly and…
Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos
Andrea Ramazzina, Vittorio Giammarino, Matteo El-Hariry +1
Imitation from videos often fails when expert demonstrations and learner environments exhibit domain shifts, such as discrepancies in lighting, color, or texture. While visual rand…
A Reinforcement Learning Approach for Robotic Unloading from Visual Observations
Vittorio Giammarino, Alberto Giammarino, Matthew Pearce
In this work, we focus on a robotic unloading problem from visual observations, where robots are required to autonomously unload stacks of parcels using RGB-D images as their prima…
Unsupervised Reward Shaping for a Robotic Sequential Picking Task from Visual Observations in a Logistics Scenario
Vittorio Giammarino, Andrew J Meyer, Kai Biegun
We focus on an unloading problem, typical of the logistics sector, modeled as a sequential pick-and-place task. In this type of task, modern machine learning techniques have shown…