102 citations · 132 across the 16 of their papers we have counts for
16 papers
YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection
Chang Liu, Liguo Zhou, Yanliang Huang +1
Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation. We introduce YOLO-BEV, an effic…
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
Mohammadhossein Malmir, Josip Josifovski, Noah Klarmann +1
Delayed Markov decision processes (DMDPs) fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance o…
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
Hongkuan Zhou, Zhenshan Bing, Xiangtong Yao +4
The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots t…
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
Zhenshan Bing, Yuan Meng, Yuqi Yun +4
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 o…
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Mingyang Wang, Zhenshan Bing, Xiangtong Yao +5
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing…
Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor
Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen +4
Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the signific…
Sequential Spatial Network for Collision Avoidance in Autonomous Driving
Haichuan Li, Liguo Zhou, Zhenshan Bing +3
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area. The purpose of collision avoidance is achieved by adjust…
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Shangding Gu, Alap Kshirsagar, Yali Du +3
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL)…
Autonomous Driving Simulator based on Neurorobotics Platform
Wei Cao, Liguo Zhou, Yuhong Huang +1
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many…
Meta-Reinforcement Learning via Language Instructions
Zhenshan Bing, Alexander Koch, Xiangtong Yao +2
Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamenta…
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions
Xiangtong Yao, Zhenshan Bing, Genghang Zhuang +4
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-…
Sim-to-Real Transfer of Robotic Assembly with Visual Inputs Using CycleGAN and Force Control
Chengjie Yuan, Yunlei Shi, Qian Feng +4
Recently, deep reinforcement learning (RL) has shown some impressive successes in robotic manipulation applications. However, training robots in the real world is nontrivial owing…
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
Rui Song, Dai Liu, Dave Zhenyu Chen +4
In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often lea…
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann +3
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific r…
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
Shangding Gu, Long Yang, Yali Du +4
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world application…
Meta-Reinforcement Learning in Broad and Non-Parametric Environments
Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin +2
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 int…