From the 1 of 7 linked papers with an AI index.
7 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…
Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning
Kevin Huang, Rosario Scalise, Cleah Winston +9
Imitation learning has proven effective for training robots to perform complex tasks from expert human demonstrations. However, it remains limited by its reliance on high-quality,…
VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated and Steerable Navigation
Mateo Guaman Castro, Sidharth Rajagopal, Daniel Gorbatov +9
A fundamental challenge in robot navigation lies in learning policies that generalize across diverse environments while conforming to the unique physical constraints and capabiliti…
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…