Autonomous Computing Lab

On Robotics Research Concentration in Autonomous Computing Lab, we are mostly engaged in creating a fusion of emerging artificial intelligence techniques and classical robotic control algorithms. Our ongoing research projects essentially aim at developing algorithms for improving dynamic adaptability of robotic agents in high non-stationary environments. Currently, we are incorporating low-dimensional geometric manifold representation with deep autoencoding technique for generating real-time and adaptive motion skills in research grade redundant robotic manipulators. Besides, we also developed various Deep Reinforcement Learning algorithms in both parametric space and non-parametric space for improving decision making skills and autonomous action-planning in robotic agents. 

 Publications List :

ArXiv pre-print : 

• Reactive Whole-Body Obstacle Avoidance for Collision-Free Human-Robot Interaction with Topological Manifold Learning.  Apan Dastider, Mingjie Lin. arxiv preprint | website 

Accepted : 

• DAMON : Dynamically Avoiding Amorphous Obstacles with Topological Manifold Learning and Deep Autoencoding. Apan Dastider, Mingjie Lin. Accepted in 2023 IEEE International Conference on Intelligent Robots and Systems (IROS) arxiv preprint | website

• Non-Parametric Stochastic Policy Gradient with Strategic Retreat for Non-Stationary Environment. Apan Dastider, Mingjie Lin. Accepted in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico City, Mexico. (in press)  arxiv preprint | website

•  Learning Adaptive Control in Dynamic Environments using Reproducing Kernel Priors with Bayesian Policy Gradients. A. Dastider, S.J.A Raza, M. Lin. Proceedings of the 2021 37th Annual ACM/SIGAPP Symposium on Applied Computing. Association for Computing Machinery, New York, NY, USA. (In Press) website/video demo 

• Safe Locomotion Within Confined Workspace using Deep Reinforcement Learning. A. Dastider, S.J.A Raza, M. Lin. 2021 Fifth IEEE International Conference on Robotic Computing (IRC), Taiwan. paper 

• Survivable Robotic Control through Guided Bayesian Policy Search with Deep Reinforcement Learning. S.J.A Raza, A. Dastider, M. Lin. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), Lyon, France. paper | website/video demo

• Survivable Hyper-Redundant Robotic Arm with Bayesian Policy Morphing. S.J.A Raza, A. Dastider, M. Lin. 2020 16th IEEE-RAS International Conference on Automation Science and Engineering (CASE), Hong Kong. paper | website

• Developmentally Synthesizing Earthworm-Like Locomotion Gaits with Bayesian-Augmented DDPG. S.J.A Raza, A. Dastider, M. Lin. 2020 16th IEEE-RAS International Conference on Automation Science and Engineering (CASE), Hong Kong. paper | website

• Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems. S.J.A. Raza, M Lin. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). paper

• Policy reuse in reinforcement learning for modular agents. S.J.A. Raza, M Lin. 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT). paper