Object Goal Navigation using Data Regularized Q-Learning
- URL: http://arxiv.org/abs/2208.13009v1
- Date: Sat, 27 Aug 2022 13:26:30 GMT
- Title: Object Goal Navigation using Data Regularized Q-Learning
- Authors: Nandiraju Gireesh, D. A. Sasi Kiran, Snehasis Banerjee, Mohan
Sridharan, Brojeshwar Bhowmick, Madhava Krishna
- Abstract summary: Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment.
Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal.
Long-term goal selection is formulated as a vision-based deep reinforcement learning problem.
- Score: 9.65323691689801
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Object Goal Navigation requires a robot to find and navigate to an instance
of a target object class in a previously unseen environment. Our framework
incrementally builds a semantic map of the environment over time, and then
repeatedly selects a long-term goal ('where to go') based on the semantic map
to locate the target object instance. Long-term goal selection is formulated as
a vision-based deep reinforcement learning problem. Specifically, an Encoder
Network is trained to extract high-level features from a semantic map and
select a long-term goal. In addition, we incorporate data augmentation and
Q-function regularization to make the long-term goal selection more effective.
We report experimental results using the photo-realistic Gibson benchmark
dataset in the AI Habitat 3D simulation environment to demonstrate substantial
performance improvement on standard measures in comparison with a state of the
art data-driven baseline.
Related papers
- Probable Object Location (POLo) Score Estimation for Efficient Object
Goal Navigation [15.623723522165731]
We introduce a novel framework centered around the Probable Object Location (POLo) score.
We further enhance the framework's practicality by introducing POLoNet, a neural network trained to approximate the computationally intensive POLo score.
Our experiments, involving the first phase of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet significantly outperforms a range of baseline methods.
arXiv Detail & Related papers (2023-11-14T08:45:32Z) - NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration.
We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments.
Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
arXiv Detail & Related papers (2023-10-11T21:07:14Z) - How To Not Train Your Dragon: Training-free Embodied Object Goal
Navigation with Semantic Frontiers [94.46825166907831]
We present a training-free solution to tackle the object goal navigation problem in Embodied AI.
Our method builds a structured scene representation based on the classic visual simultaneous localization and mapping (V-SLAM) framework.
Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
arXiv Detail & Related papers (2023-05-26T13:38:33Z) - LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects [7.491472577165315]
We present an end-to-end data-driven pipeline for determining the long-term stability of objects within a given environment, specifically distinguishing between static and dynamic objects.
Our pipeline includes a labelling method that utilizes historical data from the environment to generate training data for a neural network.
Our approach is evaluated on point cloud data from two parking lots in the NCLT dataset, and the results show that our proposed solution, outperforms direct training of a classification model for static stability vs dynamic object classification.
arXiv Detail & Related papers (2023-01-09T15:24:19Z) - Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object
Transport [83.06265788137443]
We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation.
Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning.
We propose a modular hierarchical transport policy (HTP) that builds a topological graph of the scene to perform exploration with the help of weighted frontiers.
arXiv Detail & Related papers (2022-10-28T05:30:49Z) - Navigating to Objects in Unseen Environments by Distance Prediction [16.023495311387478]
We propose an object goal navigation framework, which could directly perform path planning based on an estimated distance map.
Specifically, our model takes a birds-eye-view semantic map as input, and estimates the distance from the map cells to the target object.
With the estimated distance map, the agent could explore the environment and navigate to the target objects based on either human-designed or learned navigation policy.
arXiv Detail & Related papers (2022-02-08T09:22:50Z) - Landmark Policy Optimization for Object Navigation Task [77.34726150561087]
This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments.
Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal.
We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks.
arXiv Detail & Related papers (2021-09-17T12:28:46Z) - TDIOT: Target-driven Inference for Deep Video Object Tracking [0.2457872341625575]
In this work, we adopt the pre-trained Mask R-CNN deep object detector as the baseline.
We introduce a novel inference architecture placed on top of FPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking.
The proposed single object tracker, TDIOT, applies an appearance similarity-based temporal matching for data association.
arXiv Detail & Related papers (2021-03-19T20:45:06Z) - Learning Long-term Visual Dynamics with Region Proposal Interaction
Networks [75.06423516419862]
We build object representations that can capture inter-object and object-environment interactions over a long-range.
Thanks to the simple yet effective object representation, our approach outperforms prior methods by a significant margin.
arXiv Detail & Related papers (2020-08-05T17:48:00Z) - Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments.
We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
arXiv Detail & Related papers (2020-07-01T17:52:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.