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…
FactorLibrary: From Polynomials to Circuits via Recursive Subgoals
Rohan Pandey, Michael Ruofan Zeng, Weikun K. Zhang +5
Finding minimal arithmetic circuits for polynomials over finite fields is a combinatorially hard problem central to algebraic complexity theory. We formulate it as a reinforcement…
CircuitBuilder: From Polynomials to Circuits via Reinforcement Learning
Weikun K. Zhang, Rohan Pandey, Bhaumik Mehta +5
Motivated by auto-proof generation and Valiant's VP vs. VNP conjecture, we study the problem of discovering efficient arithmetic circuits to compute polynomials, using addition and…
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…