Search Results for author: Giulia Vezzani

Found 8 papers, 2 papers with code

Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies

no code implementations ICLR 2022 Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model.

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning

no code implementations29 Sep 2021 Abbas Abdolmaleki, Sandy Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva Tirumala, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller

Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.

Offline RL

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration

no code implementations17 Sep 2021 Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin Riedmiller

Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.

GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk

1 code implementation12 Feb 2020 Fabrizio Bottarel, Giulia Vezzani, Ugo Pattacini, Lorenzo Natale

In this paper, we present version 1. 0 of GRASPA, a benchmark to test effectiveness of grasping pipelines on physical robot setups.

Robotics

Learning latent state representation for speeding up exploration

no code implementations27 May 2019 Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel

In this work, we take a representation learning viewpoint on exploration, utilizing prior experience to learn effective latent representations, which can subsequently indicate which regions to explore.

reinforcement-learning Representation Learning

Markerless visual servoing on unknown objects for humanoid robot platforms

1 code implementation12 Oct 2017 Claudio Fantacci, Giulia Vezzani, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale

To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape.

Robotics Systems and Control Computation

Cannot find the paper you are looking for? You can Submit a new open access paper.