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Found 9 papers, 4 papers with code
Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs
We demonstrate experimental results with LLMs that address robotics task planning problems.
DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
To evaluate its performance, we conduct several tests by deploying the model to predict a set of driving records and perform real automated driving under three different conditions.
Multi-Source Soft Pseudo-Label Learning with Domain Similarity-based Weighting for Semantic Segmentation
This paper describes a method of domain adaptive training for semantic segmentation using multiple source datasets that are not necessarily relevant to the target dataset.
Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments
This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving.
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks.
End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent
Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we propose a novel deep learning model trained with end-to-end and multi-task learning manners to perform both perception and control tasks simultaneously.
Multi-task Learning with Attention for End-to-end Autonomous Driving
This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.
Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots
The core of our idea is to use multiple rich image datasets of different environments with segmentation labels to generate pseudo-labels for the target images to effectively transfer the knowledge from multiple sources and realize a precise training of semantic segmentation.
Early Estimation of User's Intention of Tele-Operation Using Object Affordance and Hand Motion in a Dual First-Person Vision
This paper describes a method of estimating the intention of a user's motion in a robot tele-operation scenario.
