5 papers
Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation
Anubhav Vishwakarma, Bhaumik Mehta, Caleb Hsu +3
Inverse Reinforcement Learning (IRL) algorithms are powerful tools for learning from and generalizing expert demonstrations, but they often rely on unconstrained exploration, rende…
Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
Tyler Han, Bat Nemekhbold, Siyang Shen +6
Current methods in robot learning are fundamentally bottlenecked by one or more of: hand-designed rewards, simulation modeling, or action supervision (e.g. teleoperation) each requ…
Model Predictive Adversarial Imitation Learning for Planning from Observation
Tyler Han, Yanda Bao, Bhaumik Mehta +8
Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm to perform plan…
Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics
Tyler Han, Preet Shah, Sidharth Rajagopal +9
Reinforcement Learning (RL) has been pivotal in recent robotics milestones and is poised to play a prominent role in the future. However, these advances can rely on proprietary sim…
Dynamics Models in the Aggressive Off-Road Driving Regime
Tyler Han, Sidharth Talia, Rohan Panicker +3
Current developments in autonomous off-road driving are steadily increasing performance through higher speeds and more challenging, unstructured environments. However, this operati…