Siddhant Gangapurwala

Orcid: 0000-0002-1308-3744

According to our database1, Siddhant Gangapurwala authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Learning and deploying robust locomotion policies with minimal dynamics randomization.
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

2023
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation.
IEEE Trans. Robotics, October, 2023

Roll-Drop: accounting for observation noise with a single parameter.
Proceedings of the Learning for Dynamics and Control Conference, 2023

Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022
RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control.
IEEE Trans. Robotics, 2022

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021
CPG-Actor: Reinforcement Learning for Central Pattern Generators.
Proceedings of the Towards Autonomous Robotic Systems - 22nd Annual Conference, 2021

Rapid Stability Margin Estimation for Contact-Rich Locomotion.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020
Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion.
IEEE Robotics Autom. Lett., 2020

First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2019
Towards Generating Simulated Walking Motion Using Position Based Deep Reinforcement Learning.
Proceedings of the Towards Autonomous Robotic Systems - 20th Annual Conference, 2019


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