Dataset Information

Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning.

ABSTRACT: Magnetically manipulated medical robots are a promising alternative to current robotic platforms, allowing for miniaturization and tetherless actuation. Controlling such systems autonomously may enable safe, accurate operation. However, classical control methods require rigorous models of magnetic fields, robot dynamics, and robot environments, which can be difficult to generate. Model-free reinforcement learning (RL) offers an alternative that can bypass these requirements. We apply RL to a robotic magnetic needle manipulation system. Reinforcement learning algorithms often require long runtimes, making them impractical for many surgical robotics applications, most of which require careful, constant monitoring. Our approach first constructs a model-based simulation (MBS) on guided real-world exploration, learning the dynamics of the environment. After intensive MBS environment training, we transfer the learned behavior from the MBS environment to the real-world. Our MBS method applies RL roughly 200 times faster than doing so in the real world, and achieves a 6 mm root-mean-square (RMS) error for a square reference trajectory. In comparison, pure simulation-based approaches fail to transfer, producing a 31 mm RMS error. These results demonstrate that MBS environments are a good solution for domains where running model-free RL is impractical, especially if an accurate simulation is not available.

SUBMITTER: Barnoy Y

PROVIDER: S-EPMC10438915 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning.

Barnoy Yotam Y Erin Onder O Raval Suraj S Pryor Will W Mair Lamar O LO Weinberg Irving N IN Diaz-Mercado Yancy Y Krieger Axel A Hager Gregory D GD

IEEE transactions on medical robotics and bionics 20221012 4

Magnetically manipulated medical robots are a promising alternative to current robotic platforms, allowing for miniaturization and tetherless actuation. Controlling such systems autonomously may enable safe, accurate operation. However, classical control methods require rigorous models of magnetic fields, robot dynamics, and robot environments, which can be difficult to generate. Model-free reinforcement learning (RL) offers an alternative that can bypass these requirements. We apply RL to a rob ...[more]

PMID: 37600471

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Project description:From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints with antagonistic muscle pairs is finding methods robust to address this ill-posed nonlinear problem. To address this computational problem, we implemented a twofold optimization and learning framework to be specialized in addressing the redundancies in the muscle control . In the first part, we used model predictive control to obtain energy efficient skeletal trajectories to mimick human movements. The second part is to use deep reinforcement learning to obtain a sequence of stimulus to be given to muscles in order to obtain the skeletal trajectories with muscle control. We observed that the desired stimulus to muscles is only efficiently constructed by integrating the state and control input in a closed-loop setting as it resembles the proprioceptive integration in the spinal cord circuits. In this work, we showed how a variety of different reference trajectories can be obtained with optimal control and how these reference trajectories are mapped to the musculoskeletal control with deep reinforcement learning. Starting from the characteristics of human arm movement to obstacle avoidance experiment, our simulation results confirm the capabilities of our optimization and learning framework for a variety of dynamic movement trajectories. In summary, the proposed framework is offering a pipeline to complement the lack of experiments to record human motion-capture data as well as study the activation range of muscles to replicate the specific trajectory of interest. Using the trajectories from optimal control as a reference signal for reinforcement learning implementation has allowed us to acquire optimum and human-like behaviour of the musculoskeletal system which provides a framework to study human movement in-silico experiments. The present framework can also allow studying upper-arm rehabilitation with assistive robots given that one can use healthy subject movement recordings as reference to work on the control architecture of assistive robotics in order to compensate behavioural deficiencies. Hence, the framework opens to possibility of replicating or complementing labour-intensive, time-consuming and costly experiments with human subjects in the field of movement studies and digital twin of rehabilitation.

Project description:Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning.Significance statementDecisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies.

Dataset Information

Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning.

Publications

Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning.

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