Martina Zambelli — accepted AI-conference papers

2025

DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots

Maria Bauzá, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins +10

ICRA 2025

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three- fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces

Cited by 12 Source ↗Scholar ↗

2022

Learning transferable motor skills with hierarchical latent mixture policies

Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani +5

ICLR 2022spotlight

For robots operating in the real world, it is desirable to learn reusable abstract behaviours that can effectively be transferred across numerous tasks and scenarios. We propose an approach to learn skills from data using a hierarchical mixture latent variable model. Our method exploits a multi-leve…

Cited by 38 Source ↗PDF ↗Scholar ↗

2020

A distributional view on multi-objective policy optimization

Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli +4

ICML 2020poster

Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel a…

Cited by 97 Source ↗PDF ↗code ↗Scholar ↗

2020

Learning rich touch representations through cross-modal self-supervision

Martina Zambelli, Yusuf Aytar, Francesco Visin, Yuxiang Zhou, Raia Hadsell

CoRL 2020

The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation. In this work we tackle the problem of learning rich touch features from cross-modal self-supervision. We evaluate them identifying objects and their properties in a few-shot classification setting

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2016

Kinematic Structure Correspondences via Hypergraph Matching

Hyung Jin Chang, Tobias Fischer, Maxime Petit, Martina Zambelli, Yiannis Demiris

CVPR 2016poster

In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed metho…

Cited by 17 PDF ↗Scholar ↗

2016

Multimodal imitation using self-learned sensorimotor representations

Martina Zambelli, Yiannis Demiris

IROS 2016poster

Although many tasks intrinsically involve multiple modalities, often only data from a single modality are used to improve complex robots acquisition of new skills. We present a method to equip robots with multimodal learning skills to achieve multimodal imitation on-the-fly on multiple concurrent ta…

Cited by 6 Source ↗Scholar ↗