47 citations · 78 across the 11 of their papers we have counts for
11 papers
Solving Markov Decision Processes with Future Information via MPC
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based p…
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg +2
Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B) algorithms. The inherent diff…
Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality
Akhil S Anand, Shambhuraj Sawant, Paavo Parmas +3
Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive. Th…
Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations
Vyacheslav Kungurtsev, Alessandro Di Frenna, Gustav Sir +5
Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots…
All AI Models are Wrong, but Some are Optimal
Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt +1
AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models…
Optimality Conditions for Model Predictive Control: Rethinking Predictive Model Design
Akhil S Anand, Arash Bahari Kordabad, Mario Zanon +1
Optimality is a critical aspect of Model Predictive Control (MPC), especially in economic MPC. However, achieving optimality in MPC presents significant challenges, and may even be…
Application of Soft Actor-Critic Algorithms in Optimizing Wastewater Treatment with Time Delays Integration
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Aviaja Anna Hansen +4
Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These…
Economic Model Predictive Control as a Solution to Markov Decision Processes
Dirk Reinhardt, Akhil S. Anand, Shambhuraj Sawant +1
Markov Decision Processes (MDPs) offer a fairly generic and powerful framework to discuss the notion of optimal policies for dynamic systems, in particular when the dynamics are st…
Data-Driven Predictive Control and MPC: Do we achieve optimality?
Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt +1
In this paper, we explore the interplay between Predictive Control and closed-loop optimality, spanning from Model Predictive Control to Data-Driven Predictive Control. Predictive…
Learning-based MPC from Big Data Using Reinforcement Learning
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
This paper presents an approach for learning Model Predictive Control (MPC) schemes directly from data using Reinforcement Learning (RL) methods. The state-of-the-art learning meth…
Deep Model Predictive Variable Impedance Control
Akhil S Anand, Fares J. Abu-Dakka, Jan Tommy Gravdahl
The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial to…