4 papers
Data-Driven Physics Embedded Dynamics with Predictive Control and Reinforcement Learning for Quadrupeds
Prakrut Kotecha, Aditya Shirwatkar, Shishir Kolathaya
State of the art quadrupedal locomotion approaches integrate Model Predictive Control (MPC) with Reinforcement Learning (RL), enabling complex motion capabilities with planning and…
STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching
Prakrut Kotecha, Ganga Nair B, Shishir Kolathaya
Robotic systems operating in unstructured environments must operate under significant uncertainty arising from intermittent contacts, frictional variability, and unmodeled complian…
Real-Time Gait Adaptation for Quadrupeds using Model Predictive Control and Reinforcement Learning
Prakrut Kotecha, Ganga Nair B, Shishir Kolathaya
Model-free reinforcement learning (RL) has enabled adaptable and agile quadruped locomotion; however, policies often converge to a single gait, leading to suboptimal performance. T…
Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion
Prakrut Kotecha, Aditya Shirwatkar, Shishir Kolathaya
Lagrangian Neural Networks (LNNs) present a principled and interpretable framework for learning the system dynamics by utilizing inductive biases. While traditional dynamics models…