29 citations · 109 across the 20 of their papers we have counts for
6 papers · 1 filter
Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults
Ayoosh Bansal, Hunmin Kim, Simon Yu +4
Advances in deep learning have revolutionized cyber-physical applications, including the development of Autonomous Vehicles. However, real-world collisions involving autonomous con…
Verifiable Obstacle Detection
Ayoosh Bansal, Hunmin Kim, Simon Yu +4
Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate f…
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping
Hongpeng Cao, Lukas Dirnberger, Daniele Bernardini +2
6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However,…
Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach
Bingzhuo Zhong, Majid Zamani, Marco Caccamo
In this paper, we provide a direct data-driven approach to synthesize safety controllers for unknown linear systems affected by unknown-but-bounded disturbances, in which identifyi…
Sandboxing (AI-based) Unverified Controllers in Stochastic Games: An Abstraction-based Approach with Safe-visor Architecture
Bingzhuo Zhong, Hongpeng Cao, Majid Zamani +1
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. AI-based) controlle…
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning
Hongpeng Cao, Mirco Theile, Federico G. Wyrwal +1
Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. However, the training of DR…