Reinforcement learning for mobile robotics exploration: A survey
Efficient exploration of unknown environments is a fundamental precondition for modern
autonomous mobile robot applications. Aiming to design robust and effective robotic …
autonomous mobile robot applications. Aiming to design robust and effective robotic …
Bayesian controller fusion: Leveraging control priors in deep reinforcement learning for robotics
We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the
strengths of traditional hand-crafted controllers and model-free deep reinforcement learning …
strengths of traditional hand-crafted controllers and model-free deep reinforcement learning …
A deep residual reinforcement learning algorithm based on Soft Actor-Critic for autonomous navigation
S Wen, Y Shu, A Rad, Z Wen, Z Guo, S Gong - Expert Systems with …, 2025 - Elsevier
The problem of autonomous navigation has attracted significant attention from robotics
research community in the last few decades. In this paper, we address the problem of low …
research community in the last few decades. In this paper, we address the problem of low …
Residual reinforcement learning from demonstrations
Residual reinforcement learning (RL) has been proposed as a way to solve challenging
robotic tasks by adapting control actions from a conventional feedback controller to …
robotic tasks by adapting control actions from a conventional feedback controller to …
Multipolar: Multi-source policy aggregation for transfer reinforcement learning between diverse environmental dynamics
Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by
exploiting knowledge from other source agents trained on relevant tasks. However, it …
exploiting knowledge from other source agents trained on relevant tasks. However, it …
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors
Combining Reinforcement Learning (RL) with a prior controller can yield the best out of two
worlds: RL can solve complex nonlinear problems, while the control prior ensures safer …
worlds: RL can solve complex nonlinear problems, while the control prior ensures safer …
Ian: Multi-behavior navigation planning for robots in real, crowded environments
State-of-the-art approaches for robot navigation among humans are typically restricted to
planar movement actions. This work addresses the question of whether it can be beneficial …
planar movement actions. This work addresses the question of whether it can be beneficial …
Apf-rl: Safe mapless navigation in unknown environments
This paper is focused on safe mapless navigation of mobile robots in unknown and possibly
complex environments containing both internal and dynamic obstacles. We present a novel …
complex environments containing both internal and dynamic obstacles. We present a novel …
Enhancing robot navigation policies with task-specific uncertainty management
Robots performing navigation tasks in complex environments face significant challenges
due to uncertainty in state estimation. Effectively managing this uncertainty is crucial, but the …
due to uncertainty in state estimation. Effectively managing this uncertainty is crucial, but the …
Residual feedback learning for contact-rich manipulation tasks with uncertainty
A Ranjbar, NA Vien, H Ziesche… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
While classic control theory offers state of the art solutions in many problem scenarios, it is
often desired to improve beyond the structure of such solutions and surpass their limitations …
often desired to improve beyond the structure of such solutions and surpass their limitations …