71 citations · 160 across the 17 of their papers we have counts for
6 papers · 1 filter
Learning Sparse Control Tasks from Pixels by Latent Nearest-Neighbor-Guided Explorations
Ruihan Zhao, Ufuk Topcu, Sandeep Chinchali +1
Recent progress in deep reinforcement learning (RL) and computer vision enables artificial agents to solve complex tasks, including locomotion, manipulation and video games from hi…
Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation
Yifan Zhou, Shubham Sonawani, Mariano Phielipp +2
Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute re…
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +3
Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., moti…
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan +3
Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we lear…
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +2
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a ta…
On Training Flexible Robots using Deep Reinforcement Learning
Zach Dwiel, Madhavun Candadai, Mariano Phielipp
The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical m…