Skip to search formSkip to main contentSkip to account menu

Learning to Explore using Active Neural SLAM

This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called `Active Neural SLAM'. Our approach leverages the strengths of both classical and…
View PDF on arXiv (opens in a new tab)

Neural Topological SLAM for Visual Navigation

TLDR
This paper designs topological representations for space that effectively leverage semantics and afford approximate geometric reasoning, and describes supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.
View on IEEE (opens in a new tab)

Learning Exploration Policies for Navigation

TLDR
This work proposes a learning-based approach and finds that the use of policies with spatial memory that are bootstrapped with imitation learning and finally finetuned with coverage rewards derived purely from on-board sensors can be effective at exploring novel environments.
View PDF on arXiv (opens in a new tab)

PyRobot: An Open-source Robotics Framework for Research and Benchmarking

TLDR
PyRobot is a light-weight, high-level interface on top of ROS that provides a consistent set of hardware independent mid-level APIs to control different robots, and will reduce the entry barrier into robotics, and democratize robotics.
View PDF on arXiv (opens in a new tab)

Semantic Visual Navigation by Watching YouTube Videos

TLDR
This paper learns and leverages semantic cues for navigating to objects of interest in novel environments, by simply watching YouTube videos, and improves upon end-to-end RL methods by 66%, while using 250x fewer interactions.
View PDF on arXiv (opens in a new tab)

BlazingText: Scaling and Accelerating Word2Vec using Multiple GPUs

TLDR
BlazingText is presented, a highly optimized implementation of word2vec in CUDA that can leverage multiple GPUs for training and can achieve a training speed of up to 43M words/sec on 8 GPUs, which is a 9x speedup over 8-threaded CPU implementations, with minimal effect on the quality of the embeddings.
View on ACM (opens in a new tab)

DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning

TLDR
This work demonstrates how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera and is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning.
View PDF on arXiv (opens in a new tab)

Semantic Curiosity for Active Visual Learning

TLDR
The exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other possible alternatives such as random exploration, prediction-error curiosity, and coverage-maximizing exploration.
View PDF on arXiv (opens in a new tab)

Learning Navigation Subroutines from Egocentric Videos

TLDR
The proposed method to learn hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks, by learning a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert Egocentric videos with agent actions is demonstrated.
View Paper (opens in a new tab)

Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects

TLDR
This paper addresses the problem of inferring contact points and the physical forces from videos of humans interacting with objects by using a physics simulator to predict effects, and enforce that estimated forces must lead to same effect as depicted in the video.
View on IEEE (opens in a new tab)
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)