Search Results for author: Florian Shkurti

Found 16 papers, 7 papers with code

Augmenting Imitation Experience via Equivariant Representations

no code implementations14 Oct 2021 Dhruv Sharma, Alihusein Kuwajerwala, Florian Shkurti

The robustness of visual navigation policies trained through imitation often hinges on the augmentation of the training image-action pairs.

Data Augmentation Visual Navigation

Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects

no code implementations30 Sep 2021 Haoping Xu, Yi Ru Wang, Sagi Eppel, Alàn Aspuru-Guzik, Florian Shkurti, Animesh Garg

To address the shortcomings of existing transparent object data collection schemes in literature, we also propose an automated dataset creation workflow that consists of robot-controlled image collection and vision-based automatic annotation.

Depth Completion

Physics-based Human Motion Estimation and Synthesis from Videos

no code implementations ICCV 2021 Kevin Xie, Tingwu Wang, Umar Iqbal, Yunrong Guo, Sanja Fidler, Florian Shkurti

We demonstrate both qualitatively and quantitatively significantly improved motion estimation, synthesis quality and physical plausibility achieved by our method on the large scale Human3. 6m dataset \cite{h36m_pami} as compared to prior kinematic and physics-based methods.

Motion Capture Motion Estimation +2

LOHO: Latent Optimization of Hairstyles via Orthogonalization

1 code implementation CVPR 2021 Rohit Saha, Brendan Duke, Florian Shkurti, Graham W. Taylor, Parham Aarabi

Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based approach using GAN inversion to infill missing hair structure details in latent space during hairstyle transfer.

GAN inversion SSIM

Latent Skill Planning for Exploration and Transfer

no code implementations ICLR 2021 Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti

To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge.

Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models

no code implementations2 Nov 2020 Yuchen Wu, Melissa Mozifian, Florian Shkurti

Unlike the majority of existing methods that assume optimal demonstrations and incorporate the demonstration data as hard constraints on policy optimization, we instead incorporate demonstration data as advice in the form of a reward shaping potential trained as a generative model of states and actions.

Imitation Learning

Conservative Safety Critics for Exploration

no code implementations ICLR 2021 Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg

Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning.

Safe Exploration

Continual Model-Based Reinforcement Learning with Hypernetworks

1 code implementation25 Sep 2020 Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, Florian Shkurti

Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model.

Continual Learning Model-based Reinforcement Learning

LEAF: Latent Exploration Along the Frontier

no code implementations21 May 2020 Homanga Bharadhwaj, Animesh Garg, Florian Shkurti

We target the challenging problem of policy learning from initial and goal states specified as images, and do not assume any access to the underlying ground-truth states of the robot and the environment.

Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

1 code implementation19 Apr 2020 Homanga Bharadhwaj, Kevin Xie, Florian Shkurti

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions.

Model-based Reinforcement Learning

One-Shot Informed Robotic Visual Search in the Wild

1 code implementation22 Mar 2020 Karim Koreitem, Florian Shkurti, Travis Manderson, Wei-Di Chang, Juan Camilo Gamboa Higuera, Gregory Dudek

In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection.

Representation Learning Robot Navigation +1

Diversity inducing Information Bottleneck in Model Ensembles

1 code implementation10 Mar 2020 Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti

Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research.

Out-of-Distribution Detection

Underwater Multi-Robot Convoying using Visual Tracking by Detection

1 code implementation25 Sep 2017 Florian Shkurti, Wei-Di Chang, Peter Henderson, Md Jahidul Islam, Juan Camilo Gamboa Higuera, Jimmy Li, Travis Manderson, Anqi Xu, Gregory Dudek, Junaed Sattar

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.

Object Detection Visual Tracking

Benchmark Environments for Multitask Learning in Continuous Domains

1 code implementation14 Aug 2017 Peter Henderson, Wei-Di Chang, Florian Shkurti, Johanna Hansen, David Meger, Gregory Dudek

As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit.

OpenAI Gym

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