29 citations · 70 across the 6 of their papers we have counts for
10 papers
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
Brian Ichter, Pierre Sermanet, Corey Lynch
Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planni…
Counting Out Time: Class Agnostic Video Repetition Counting in the Wild
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson +2
We present an approach for estimating the period with which an action is repeated in a video. The crux of the approach lies in constraining the period prediction module to use temp…
Learning to Play by Imitating Humans
Rostam Dinyari, Pierre Sermanet, Corey Lynch
Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions. Recently it has been shown that…
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…
Online Object Representations with Contrastive Learning
Sören Pirk, Mohi Khansari, Yunfei Bai +2
We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.…
Temporal Cycle-Consistency Learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson +2
We introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycle consistency (…