5 papers
Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering
Aditya Shirwatkar, Sebastian Sanokowski, Shishir Kolathaya +2
Reinforcement learning (RL) policies enable dynamic legged locomotion but lack mechanisms to avoid violations of safety constraints that are absent during training. Large-scale off…
VIP-Loco: A Visually Guided Infinite Horizon Planning Framework for Legged Locomotion
Aditya Shirwatkar, Satyam Gupta, Shishir Kolathaya
Perceptive locomotion for legged robots requires anticipating and adapting to complex, dynamic environments. Model Predictive Control (MPC) serves as a strong baseline, providing i…
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…
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…
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion
Aditya Shirwatkar, Naman Saxena, Kishore Chandra +1
A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints and provide interpretability of the sequence of commands ov…