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Found 7 papers, 1 papers with code
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance.
Physical Deep Reinforcement Learning: Safety and Unknown Unknowns
In this paper, we propose the Phy-DRL: a physics-model-regulated deep reinforcement learning framework for safety-critical autonomous systems.
Physical Deep Reinforcement Learning Towards Safety Guarantee
Deep reinforcement learning (DRL) has achieved tremendous success in many complex decision-making tasks of autonomous systems with high-dimensional state and/or action spaces.
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping
To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation.
Sandboxing (AI-based) Unverified Controllers in Stochastic Games: An Abstraction-based Approach with Safe-visor Architecture
In this paper, we propose a construction scheme for a Safe-visor architecture for sandboxing unverified controllers, e. g., artificial intelligence-based (a. k. a.
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning
To overcome the reality gap, our architecture exploits sim-to-real transfer strategies to continue the training of simulation-pretrained agents on a physical system.
Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
To cope with this difficulty, we propose in this work a Safe-visor architecture for sandboxing unverified controllers in CPSs operating in noisy environments (a. k. a.
