From the 1 of 5 linked papers with an AI index.
5 papers
Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation
Anubhav Vishwakarma, Bhaumik Mehta, Caleb Hsu +3
The paper proposes a method that learns safety‑ensuring barrier functions directly from unlabeled expert observations by restricting inverse reinforcement learning to the space of…
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…
Distributional Successor Features Enable Zero-Shot Policy Optimization
Chuning Zhu, Xinqi Wang, Tyler Han +2
Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in princi…