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Found 14 papers, 4 papers with code
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model's online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from new samples, in conjunction with the learned hyperparameters.
Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments
This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static and moving obstacles.
Contact Energy Based Hindsight Experience Prioritization
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences.
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving
More research attention has recently been given to end-to-end autonomous driving technologies where the entire driving pipeline is replaced with a single neural network because of its simpler structure and faster inference time.
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world.
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters.
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
Selective Frequency Network for Image Restoration
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
Ranked #1 on
Deblurring
on RSBlur
Sequential Spatial Network for Collision Avoidance in Autonomous Driving
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area.
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions
We propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetrical behaviors and language instructions.
Towards Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network
As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment.
Meta-Reinforcement Learning in Broad and Non-Parametric Environments
Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills.
Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation
In this paper, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment.
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle
Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications.
