4 papers
Non-Parametric Stochastic Policy Gradient with Strategic Retreat for Non-Stationary Environment
Apan Dastider, Mingjie Lin
In modern robotics, effectively computing optimal control policies under dynamically varying environments poses substantial challenges to the off-the-shelf parametric policy gradie…
Hardware-Efficient Deconvolution-Based GAN for Edge Computing
Azzam Alhussain, Mingjie Lin
Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution. However, its performance comes at a signif…
Survivable Robotic Control through Guided Bayesian Policy Search with Deep Reinforcement Learning
Sayyed Jaffar Ali Raza, Apan Dastider, Mingjie Lin
Many robot manipulation skills can be represented with deterministic characteristics and there exist efficient techniques for learning parameterized motor plans for those skills. H…
Survivable Hyper-Redundant Robotic Arm with Bayesian Policy Morphing
Sayyed Jaffar Ali Raza, Apan Dastider, Mingjie Lin
In this paper we present a Bayesian reinforcement learning framework that allows robotic manipulators to adaptively recover from random mechanical failures autonomously, hence bein…