Search Results for author: Daniel Kappler

Found 8 papers, 2 papers with code

BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

no code implementations4 Feb 2022 Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn

In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.

Imitation Learning

Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data

no code implementations13 May 2020 Mohi Khansari, Daniel Kappler, Jianlan Luo, Jeff Bingham, Mrinal Kalakrishnan

Similar to computer vision problems, such as object detection, Action Image builds on the idea that object features are invariant to translation in image space.

Object Detection Representation Learning

Scalable Multi-Task Imitation Learning with Autonomous Improvement

no code implementations25 Feb 2020 Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn

In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.

Imitation Learning reinforcement-learning

Leveraging Contact Forces for Learning to Grasp

1 code implementation19 Sep 2018 Hamza Merzic, Miroslav Bogdanovic, Daniel Kappler, Ludovic Righetti, Jeannette Bohg

While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

Online Learning of a Memory for Learning Rates

1 code implementation20 Sep 2017 Franziska Meier, Daniel Kappler, Stefan Schaal

The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks.

Meta-Learning online learning

Superpixel Convolutional Networks using Bilateral Inceptions

no code implementations20 Nov 2015 Raghudeep Gadde, Varun Jampani, Martin Kiefel, Daniel Kappler, Peter V. Gehler

We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image.

Semantic Segmentation Superpixels

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