202 citations · 563 across the 65 of their papers we have counts for
7 papers · 1 filter
Latent Policies for Adversarial Imitation Learning
Tianyu Wang, Nikhil Karnwal, Nikolay Atanasov
This paper considers learning robot locomotion and manipulation tasks from expert demonstrations. Generative adversarial imitation learning (GAIL) trains a discriminator that disti…
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata
Tianyu Wang, Nikolay Atanasov
This paper presents a method for learning logical task specifications and cost functions from demonstrations. Constructing specifications by hand is challenging for complex objecti…
Coding for Distributed Multi-Agent Reinforcement Learning
Baoqian Wang, Junfei Xie, Nikolay Atanasov
This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems. Stragglers arise frequently in a distribut…
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning
Tianyu Wang, Vikas Dhiman, Nikolay Atanasov
This paper focuses on inverse reinforcement learning for autonomous navigation using distance and semantic category observations. The objective is to infer a cost function that exp…
Learning Navigation Costs from Demonstration with Semantic Observations
Tianyu Wang, Vikas Dhiman, Nikolay Atanasov
This paper focuses on inverse reinforcement learning (IRL) for autonomous robot navigation using semantic observations. The objective is to infer a cost function that explains demo…
Learning Navigation Costs from Demonstration in Partially Observable Environments
Tianyu Wang, Vikas Dhiman, Nikolay Atanasov
This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments. The objective is to infe…
Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
Steven W. Chen, Tianyu Wang, Nikolay Atanasov +2
This work presents an explicit-implicit procedure to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability. The approach co…