activity
20172023
most citedOpen X-Embodiment: Robotic Learning Datasets and RT-X Models

103 citations · 144 across the 9 of their papers we have counts for

collaborators

10 papers

cs.RO2023★ 103 cited

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…

cs.CV2023★ 1 cited

Can Language Models Learn to Listen?

Evonne Ng, Sanjay Subramanian, Dan Klein +3

We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Given an input transcription of the s…

cs.RO2023★ 4 cited

Robot Learning with Sensorimotor Pre-training

Ilija Radosavovic, Baifeng Shi, Letian Fu +3

We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a seq…

cs.CV2023★ 4 cited

Fast Image-based Neural Relighting with Translucency-Reflection Modeling

Shizhan Zhu, Shunsuke Saito, Aljaz Bozic +3

Image-based lighting (IBL) is a widely used technique that renders objects using a high dynamic range image or environment map. However, aggregating the irradiance at the object's…

cs.CV2023★ 5 cited

TOAST: Transfer Learning via Attention Steering

Baifeng Shi, Siyu Gai, Trevor Darrell +1

Transfer learning involves adapting a pre-trained model to novel downstream tasks. However, we observe that current transfer learning methods often fail to focus on task-relevant f…

cs.CL2023★ 20 cited

Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Sheng Shen, Le Hou, Yanqi Zhou +17

Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.…

cs.RO2023★ 3 cited

Real-World Humanoid Locomotion with Reinforcement Learning

Ilija Radosavovic, Tete Xiao, Bike Zhang +3

Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new pl…

cs.RO2021

Towards Learning to Play Piano with Dexterous Hands and Touch

Huazhe Xu, Yuping Luo, Shaoxiong Wang +2

The virtuoso plays the piano with passion, poetry and extraordinary technical ability. As Liszt said (a virtuoso)must call up scent and blossom, and breathe the breath of life. The…

cs.RO2021

PyTouch: A Machine Learning Library for Touch Processing

Mike Lambeta, Huazhe Xu, Jingwei Xu +4

With the increased availability of rich tactile sensors, there is an equally proportional need for open-source and integrated software capable of efficiently and effectively proces…

cs.CV2017★ 4 cited

Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)

Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata +4

Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to…