Search Results for author: Karl Schmeckpeper

Found 12 papers, 9 papers with code

Semantic keypoint-based pose estimation from single RGB frames

no code implementations12 Apr 2022 Karl Schmeckpeper, Philip R. Osteen, Yufu Wang, Georgios Pavlakos, Kenneth Chaney, Wyatt Jordan, Xiaowei Zhou, Konstantinos G. Derpanis, Kostas Daniilidis

Empirically, we show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios even against a cluttered background.

Pose Estimation

Uncertainty-driven Planner for Exploration and Navigation

1 code implementation24 Feb 2022 Georgios Georgakis, Bernadette Bucher, Anton Arapin, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis

We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging.

Object-centric Video Prediction without Annotation

1 code implementation6 May 2021 Karl Schmeckpeper, Georgios Georgakis, Kostas Daniilidis

Object-centric video prediction offers a solution to these problems by taking advantage of the simple prior that the world is made of objects and by providing a more natural interface for control.

Video Prediction

Deformable Linear Object Prediction Using Locally Linear Latent Dynamics

1 code implementation26 Mar 2021 Wenbo Zhang, Karl Schmeckpeper, Pratik Chaudhari, Kostas Daniilidis

We empirically demonstrate that our approach can predict the rope state accurately up to ten steps into the future and that our algorithm can find the optimal action given an initial state and a goal state.

Reinforcement Learning with Videos: Combining Offline Observations with Interaction

1 code implementation12 Nov 2020 Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn

In this paper, we consider the question: can we perform reinforcement learning directly on experience collected by humans?


An Adversarial Objective for Scalable Exploration

1 code implementation13 Mar 2020 Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis

Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature.

Active Learning

Reactive Navigation in Partially Familiar Planar Environments Using Semantic Perceptual Feedback

2 code implementations20 Feb 2020 Vasileios Vasilopoulos, Georgios Pavlakos, Karl Schmeckpeper, Kostas Daniilidis, Daniel E. Koditschek

This paper solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in SLAM and visual object recognition to recast prior geometric knowledge in terms of an offline catalogue of familiar objects.


Learning Predictive Models From Observation and Interaction

no code implementations ECCV 2020 Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn

Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes.

RoboNet: Large-Scale Multi-Robot Learning

no code implementations24 Oct 2019 Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn

This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?

Video Prediction

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