activity
20192022
most citedData-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks

29 citations · 48 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2022

Gesture2Path: Imitation Learning for Gesture-aware Navigation

Catie Cuan, Edward Lee, Emre Fisher +5

As robots increasingly enter human-centered environments, they must not only be able to navigate safely around humans, but also adhere to complex social norms. Humans often rely on…

cs.RO20226 cited

A Protocol for Validating Social Navigation Policies

Sören Pirk, Edward Lee, Xuesu Xiao +3

Enabling socially acceptable behavior for situated agents is a major goal of recent robotics research. Robots should not only operate safely around humans, but also abide by comple…

cs.RO2020

Modeling Long-horizon Tasks as Sequential Interaction Landscapes

Sören Pirk, Karol Hausman, Alexander Toshev +1

Complex object manipulation tasks often span over long sequences of operations. Task planning over long-time horizons is a challenging and open problem in robotics, and its complex…

cs.RO201929 cited

Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks

Xinchen Yan, Mohi Khansari, Jasmine Hsu +4

Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data. To work well in…

cs.CV201913 cited

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.…