565 citations · 648 across the 8 of their papers we have counts for
8 papers · 1 filter
DexterityGen: Foundation Controller for Unprecedented Dexterity
Zhao-Heng Yin, Changhao Wang, Luis Pineda +11
Teaching robots dexterous manipulation skills, such as tool use, presents a significant challenge. Current approaches can be broadly categorized into two strategies: human teleoper…
Sparsh: Self-supervised touch representations for vision-based tactile sensing
Carolina Higuera, Akash Sharma, Chaithanya Krishna Bodduluri +8
In this work, we introduce general purpose touch representations for the increasingly accessible class of vision-based tactile sensors. Such sensors have led to many recent advance…
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Julian Ibarz, Jie Tan, Chelsea Finn +3
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low level sensor observations. Although a large portion of de…
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Mohi Khansari, Daniel Kappler, Jianlan Luo +2
This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves grasp success on re…
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
Cristian Bodnar, Adrian Li, Karol Hausman +2
The distributional perspective on reinforcement learning (RL) has given rise to a series of successful Q-learning algorithms, resulting in state-of-the-art performance in arcade ga…
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Mengyuan Yan, Adrian Li, Mrinal Kalakrishnan +1
Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time…