4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
Shathushan Sivashangaran, Vihaan Dutta, Apoorva Khairnar +2
Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestrian fatalities and $500 billio…
Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing
Shathushan Sivashangaran, Apoorva Khairnar, Sepideh Gohari +2
Autonomous racing without prebuilt maps is a grand challenge for embedded robotics that requires kinodynamic planning from instantaneous sensor data at the acceleration and tire fr…
OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing
Yogesh Phalak, Wen Sin Lor, Apoorva Khairnar +3
Physical Reservoir Computing (PRC) leverages the intrinsic nonlinear dynamics of physical substrates, mechanical, optical, spintronic, and beyond, as fixed computational reservoirs…
Mobile Robot Exploration Without Maps via Out-of-Distribution Deep Reinforcement Learning
Shathushan Sivashangaran, Apoorva Khairnar, Azim Eskandarian
Autonomous Mobile Robot (AMR) navigation in dynamic environments that may be GPS denied, without a-priori maps, is an unsolved problem with potential to improve humanity's capabili…
AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning
Shathushan Sivashangaran, Apoorva Khairnar, Azim Eskandarian
Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazar…