Search Results for author: Alexander Toshev

Found 27 papers, 10 papers with code

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

no code implementations4 Apr 2022 Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan

We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.

Decision Making Language Modelling

Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation

no code implementations28 Mar 2022 Haresh Karnan, Anirudh Nair, Xuesu Xiao, Garrett Warnell, Soeren Pirk, Alexander Toshev, Justin Hart, Joydeep Biswas, Peter Stone

Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans.

Imitation Learning Robot Navigation

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation

no code implementations18 Aug 2020 Fei Xia, Chengshu Li, Roberto Martín-Martín, Or Litany, Alexander Toshev, Silvio Savarese

To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base.

Continuous Control Hierarchical Reinforcement Learning +1

ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects

3 code implementations23 Jun 2020 Dhruv Batra, Aaron Gokaslan, Aniruddha Kembhavi, Oleksandr Maksymets, Roozbeh Mottaghi, Manolis Savva, Alexander Toshev, Erik Wijmans

In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e. g., find a chair, by navigating to it.

Modeling Long-horizon Tasks as Sequential Interaction Landscapes

no code implementations8 Jun 2020 Sören Pirk, Karol Hausman, Alexander Toshev, Mohi Khansari

We show that complex plans can be carried out when executing the robotic task and the robot can interactively adapt to changes in the environment and recover from failure cases.

Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments

1 code implementation30 Oct 2019 Fei Xia, William B. Shen, Chengshu Li, Priya Kasimbeg, Micael Tchapmi, Alexander Toshev, Li Fei-Fei, Roberto Martín-Martín, Silvio Savarese

We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task.

Robot Navigation

Long Range Neural Navigation Policies for the Real World

no code implementations23 Mar 2019 Ayzaan Wahid, Alexander Toshev, Marek Fiser, Tsang-Wei Edward Lee

Learned Neural Network based policies have shown promising results for robot navigation.

Robot Navigation

Self-supervisory Signals for Object Discovery and Detection

no code implementations8 Jun 2018 Etienne Pot, Alexander Toshev, Jana Kosecka

In robotic applications, we often face the challenge of discovering new objects while having very little or no labelled training data.

Object Discovery

Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control

no code implementations CVPR 2018 Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine

In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback.

Visual Representations for Semantic Target Driven Navigation

3 code implementations15 May 2018 Arsalan Mousavian, Alexander Toshev, Marek Fiser, Jana Kosecka, Ayzaan Wahid, James Davidson

We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.

Domain Adaptation Visual Navigation

Sim2Real View Invariant Visual Servoing by Recurrent Control

no code implementations20 Dec 2017 Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine

To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object.

No Fuss Distance Metric Learning using Proxies

2 code implementations ICCV 2017 Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, Saurabh Singh

Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized.

Metric Learning Semantic Similarity +2

Towards Accurate Multi-person Pose Estimation in the Wild

no code implementations CVPR 2017 George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, Kevin Murphy

Trained on COCO data alone, our final system achieves average precision of 0. 649 on the COCO test-dev set and the 0. 643 test-standard sets, outperforming the winner of the 2016 COCO keypoints challenge and other recent state-of-art.

Human Detection Keypoint Detection +1

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

19 code implementations21 Sep 2016 Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing.

Image Captioning Translation

Chained Predictions Using Convolutional Neural Networks

no code implementations8 May 2016 Georgia Gkioxari, Alexander Toshev, Navdeep Jaitly

In this model the output variables for a given input are predicted sequentially using neural networks.

Pose Estimation

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

1 code implementation20 Nov 2015 Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.

Active Learning

Generation and Comprehension of Unambiguous Object Descriptions

1 code implementation CVPR 2016 Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan Yuille, Kevin Murphy

We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described.

Image Captioning Referring Expression

Deep Convolutional Ranking for Multilabel Image Annotation

no code implementations17 Dec 2013 Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, Sergey Ioffe

Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications.

Scalable Object Detection using Deep Neural Networks

6 code implementations CVPR 2014 Dumitru Erhan, Christian Szegedy, Alexander Toshev, Dragomir Anguelov

Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012).

Object Detection Object Recognition

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