Search Results for author: Francesco Nori

Found 10 papers, 1 papers with code

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields

no code implementations10 Oct 2022 Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess

A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF volume density) and the dynamic objects' geometry and physical properties (assumed known).

Novel View Synthesis

Learning Dexterous Manipulation from Suboptimal Experts

no code implementations16 Oct 2020 Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori

Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.

Offline RL Q-Learning

Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer

no code implementations21 Oct 2019 Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe, Tom Rothorl, Abbas Abdolmaleki, Tom Erez, Yuval Tassa, Francesco Nori

Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.

reinforcement-learning Reinforcement Learning (RL)

Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation

no code implementations21 Oct 2019 Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe, Konstantinos Bousmalis, Francesco Nori

In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.

Domain Adaptation reinforcement-learning +1

Incremental Semiparametric Inverse Dynamics Learning

no code implementations18 Jan 2016 Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori

This paper presents a novel approach for incremental semiparametric inverse dynamics learning.

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