Blar i Institutt for teknisk kybernetikk på forfatter "Sadanandan Anand, Akhil"
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A deep reinforcement learning based approach towards generating human walking behavior with a neuromuscular model
Sadanandan Anand, Akhil; Zhao, Guoping; Roth, Hubert; Seyfarth, Andre (Peer reviewed; Journal article, 2019)A gait model capable of generating human-like walking behavior at both the kinematic and the muscular level can be a very useful framework for developing control schemes for humanoids and wearable robots such as exoskeletons ... -
Evaluation of Variable Impedance-and Hybrid Force/MotionControllers for Learning Force Tracking Skills
Sadanandan Anand, Akhil; Myrestrand, Martin Hagen; Gravdahl, Jan Tommy (Chapter, 2022)For robots to perform real-world force interaction tasks with human level dexterity, it is crucial to develop adaptable and compliant force controllers. Learning techniques, especially reinforcement learning, provide a ... -
Model-based variable impedance learning control for robotic manipulation
Sadanandan Anand, Akhil; Gravdahl, Jan Tommy; Abu-Dakka, Fares J. (Journal article; Peer reviewed, 2023)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 for enabling real-world force interaction ... -
A Painless Deterministic Policy Gradient Method for Learning-based MPC
Sadanandan Anand, Akhil; Sawant, Shambhuraj Vijaysinh; Gros, Sebastien Nicolas; Gravdahl, Jan Tommy (Chapter, 2023)The combination of Reinforcement Learning (RL) and Model Predictive Control (MPC) has gained a lot of interest in the recent literature as a way of computing the optimal policies from MPC schemes based on inaccurate models. ... -
Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments
Håkansson, Anne; Saad, Aya; Sadanandan Anand, Akhil; Gjærum, Vilde Benoni; Robinson, Haakon; Seel, Katrine (Peer reviewed; Journal article, 2021)Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, ... -
Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review
Sadanandan Anand, Akhil; Seel, Katrine; Gjærum, Vilde Benoni; Håkansson, Anne; Robinson, Haakon; Saad, Aya (Peer reviewed; Journal article, 2021)Real-world autonomous systems are often controlled using conventional model-based control methods. But if accurate models of a system are not available, these methods may be unsuitable. For many safety-critical systems, ...