activity
20192023
most citedLearning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning

45 citations · 80 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2023★ 9 cited

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…

cs.RO2021★ 2 cited

Learning rich touch representations through cross-modal self-supervision

Martina Zambelli, Yusuf Aytar, Francesco Visin +2

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 cros…

cs.LG2020★ 24 cited

A Distributional View on Multi-Objective Policy Optimization

Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever +7

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…

cs.RO2019

Multimodal representation models for prediction and control from partial information

Martina Zambelli, Antoine Cully, Yiannis Demiris

Similar to humans, robots benefit from interacting with their environment through a number of different sensor modalities, such as vision, touch, sound. However, learning from diff…

cs.RO2019★ 45 cited

Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning

Sandy H. Huang, Martina Zambelli, Jackie Kay +4

Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear. We propose an approach that enables deep rein…