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Found 7 papers, 2 papers with code
Multiplier analysis of Lurye systems with power signals
Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities.
Sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-place
This paper proposes a self-supervised vision-based DRL method that allows robots to pick and place objects effectively and efficiently when directly transferring a training model from simulation to the real world.
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations.
On the Necessity and Sufficiency of Discrete-Time O'Shea-Zames-Falb Multipliers
Roughly speaking, a successful proof of the conjecture would require: (a) a conic parameterization of a set of multipliers that describes exactly the set of nonlinearities, (b) a lossless S-procedure to show that the non-existence of a multiplier implies that the Lurye system is not uniformly robustly stable over the set of nonlinearities, and (c) the existence of a multiplier in the set of multipliers used in (a) implies the existence of an LTI multiplier.
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data.
3D Vision-guided Pick-and-Place Using Kuka LBR iiwa Robot
This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera.
Input-Output Stability of Barrier-Based Model Predictive Control
Conditions for input-output stability of barrier-based model predictive control of linear systems with linear and convex nonlinear (hard or soft) constraints are established through the construction of integral quadratic constraints (IQCs).
Systems and Control
