Skip to content
GCC AI Research

Search

Results for "critical states"

Learning to Identify Critical States for Reinforcement Learning from Videos

arXiv ·

Researchers at KAUST have developed a new method called Deep State Identifier for extracting information from videos for reinforcement learning. The method learns to predict returns from video-encoded episodes and identifies critical states using mask-based sensitivity analysis. Experiments demonstrate the method's potential for understanding and improving agent behavior in DRL.

From State Estimation on Lie Groups to Robot Imagination

MBZUAI ·

Gregory Chirikjian presented an overview talk on applying probability, harmonic analysis, and geometry to robotics, emphasizing the need for robots to function beyond traditional industrial programming. He discussed a new approach where robots define affordances of objects, using simulation to 'imagine' object use and enabling reasoning about novel objects. Probabilistic methods on Lie-groups, initially developed for mobile robot state estimation, are now adapted for one-shot learning of affordances, with plans to integrate large language models. Why it matters: This research direction aims to enhance robot intelligence and adaptability, crucial for service robots in dynamic environments and aligning with broader goals of advanced AI integration in robotics.

CRC Seminar Series - Conor McMenamin

TII ·

Conor McMenamin from Universitat Pompeu Fabra presented a seminar on State Machine Replication (SMR) without honest participants. The talk covered the limitations of current SMR protocols and introduced the ByRa model, a framework for player characterization free of honest participants. He then described FAIRSICAL, a sandbox SMR protocol, and discussed how the ideas could be extended to real-world protocols, with a focus on blockchains and cryptocurrencies. Why it matters: This research on SMR protocols and their incentive compatibility could lead to more robust and secure blockchain technologies in the region.

Saving ghost cities

KAUST ·

In a 2018 KAUST lecture, MIT professor Kamal Youcef-Toumi discussed the case of Ordos Kangbashi, a Chinese city designed for a million residents that became a near-ghost town. Despite government incentives, the city struggled due to an economic downturn and lack of social and economic balance. Youcef-Toumi emphasized the importance of the public realm and a balance between social and economic development for successful cities. Why it matters: The analysis provides insights relevant to urban planning in Saudi Arabia and the broader GCC region, where new cities and megaprojects are being developed.