Decentralized nearly optimal control of a class of interconnected nonlinear discrete-time systems by using online Hamilton-Bellman-Jacobi formulation

Mehraeen, Shahab; Jagannathan, S.

doi:10.1109/ijcnn.2010.5596704

Cited by 1 publication

(1 citation statement)

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“…Classical neuroadaptive control methods are explored in [25] for adapting to varying joint dynamics with decentralized local control. In [26], the Hamilton-Jacobi-Bellman (HJB) formulation from the classical control literature is used with a neural network function approximator to generate controllers. With the advent of deep reinforcement learning (DRL), there is a push towards using DRL for robotics, including re-configurable robots.…”

Section: A Re-configurable Robotic Manipulatorsmentioning

confidence: 99%

Malleable Agents for Re-Configurable Robotic Manipulators

Kumar¹,

Hosangadi²

2022

Preprint

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Re-configurable robots potentially have more utility and flexibility for many real-world tasks. Designing a learning agent to operate such robots requires adapting to different configurations. While deep reinforcement learning has had immense success in robotic manipulation, domain adaptation is a significant problem that limits its applicability to real-world robotics. We focus on robotic arms with multiple rigid links connected by joints. Recent attempts have performed domain adaptation and Sim2Real transfer to provide robustness to robotic arm dynamics and sensor/camera variations. However, there have been no previous attempts to adapt to robotic arms with a varying number of links. We propose an RL agent with sequence neural networks embedded in the deep neural network to adapt to robotic arms that have a varying number of links. Further, with the additional tool of domain randomization, this agent adapts to different configurations with varying number/length of links and dynamics noise. We perform simulations on a 2D N-link arm to show the ability of our network to transfer and generalize efficiently.

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Section: A Re-configurable Robotic Manipulatorsmentioning

confidence: 99%