4 papers
SAFE--MA--RRT: Multi-Agent Motion Planning with Data-Driven Safety Certificates
Babak Esmaeili, Hamidreza Modares
This paper proposes a fully data-driven motion-planning framework for homogeneous linear multi-agent systems that operate in shared, obstacle-filled workspaces without access to ex…
Data-Driven Motion Planning for Uncertain Nonlinear Systems
Babak Esmaeili, Hamidreza Modares, Stefano Di Cairano
This paper proposes a data-driven motion-planning framework for nonlinear systems that constructs a sequence of overlapping invariant polytopes. Around each randomly sampled waypoi…
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems
Babak Esmaeili, Nariman Niknejad, Hamidreza Modares
This paper presents a risk-aware safe reinforcement learning (RL) control design for stochastic discrete-time linear systems. Rather than using a safety certifier to myopically int…
Variational Stochastic Gradient Descent for Deep Neural Networks
Haotian Chen, Anna Kuzina, Babak Esmaeili +1
Current state-of-the-art optimizers are adaptive gradient-based optimization methods such as Adam. Recently, there has been an increasing interest in formulating gradient-based opt…