Model-Less Feedback Control of Continuum Manipulators in Constrained Environments

By Michael C. Yip, David B. Camarillo. 2014

253 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering

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Citations in the corpus

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017, methodology): “However, the most efficient model-based algorithms have used relatively simple function approximators, such as Gaussian processes [6], [21], [22], time-varying linear models [5], [23], [24], and mixtures of Gaussians [25].”
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees (2018, neutral): “…(Deisenroth & Rasmussen, 2011b; Ko & Fox, 2009), time-varying linear models (Levine & Koltun, 2013; Lioutikov et al., 2014; Levine & Abbeel, 2014; Yip & Camarillo, 2014), mixture of Gaussians (Khansari-Zadeh & Billard, 2011), to neural networks (Hunt et al., 1992; Nagabandi et al., 2017;…”
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model (2016, methodology): “Our approach is agnostic to how the policy πsource was obtained.”
Continuum Robots for Medical Applications: A Survey (2015, methodology): “For tendon-driven catheter robots, Camarillo et al., have demonstrated a similar approach that optimizes positive tendon tension while achieving control in configuration space [150] and task space [151].”
Control Strategies for Soft Robotic Manipulators: A Survey (2018, unknown): “This is probably why modelfree approaches have fared better for systems that are highly nonlinear, nonuniform,(39) influenced by gravity,(41,48) or act within unstructured environments where modeling is almost impossible.(42) However, for well-behaved compact manipulators in known environments, mode”
Model Based Planning with Energy Based Models (2019, background): “2 Related Work A number of model classes have been explored for model-based planning in robotics and artificial intelligence literature, from feed-forward, recurrent [11], temporal segment [12] and Bayesian [13] neural networks, to locally-linear models [14, 15] and Gaussian processes [16].”
Combining model-based policy search with online model learning for control of physical humanoids (2016, methodology): “The local models can either be linear [10], [28], or nonlinear [13].”
A decade retrospective of medical robotics research from 2010 to 2020 (2021, methodology): “Alternatively, a model-free approach has been proposed in which the contactconstrained kinematic model is estimated during task execution (65).”