38 citations · 122 across the 10 of their papers we have counts for
10 papers
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier +13
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common a…
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever +8
For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an…
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
Oliver Groth, Markus Wulfmeier, Giulia Vezzani +5
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks. However, as the ag…
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani +11
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 ob…
"What, not how": Solving an under-actuated insertion task from scratch
Giulia Vezzani, Michael Neunert, Markus Wulfmeier +7
Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics…
GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk
Fabrizio Bottarel, Giulia Vezzani, Ugo Pattacini +1
The use of benchmarks is a widespread and scientifically meaningful practice to validate performance of different approaches to the same task. In the context of robot grasping the…
Learning latent state representation for speeding up exploration
Giulia Vezzani, Abhishek Gupta, Lorenzo Natale +1
Exploration is an extremely challenging problem in reinforcement learning, especially in high dimensional state and action spaces and when only sparse rewards are available. Effect…
Markerless visual servoing on unknown objects for humanoid robot platforms
Claudio Fantacci, Giulia Vezzani, Ugo Pattacini +2
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. In this work we propose a fr…
Memory Unscented Particle Filter for 6-DOF Tactile Localization
Giulia Vezzani, Ugo Pattacini, Giorgio Battistelli +2
This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e. the pose estimation of tridimensional objects given tactile measurements. This estimation problem is funda…