Towards Safe Control of Continuum Manipulator Using Shielded Multiagent Reinforcement Learning

• URL: http://arxiv.org/abs/2106.07892v1
• Date: Tue, 15 Jun 2021 05:55:05 GMT
• Title: Towards Safe Control of Continuum Manipulator Using Shielded Multiagent Reinforcement Learning
• Authors: Guanglin Ji, Junyan Yan, Jingxin Du, Wanquan Yan, Jibiao Chen, Yongkang Lu, Juan Rojas, and Shing Shin Cheng
• Abstract summary: The control of the robot is formulated as a one-DoF, one agent problem in the MADQN framework to improve the learning efficiency. Shielded MADQN enabled the robot to perform point and trajectory tracking with submillimeter root mean square errors under external loads.
• Score: 1.2647816797166165
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Continuum robotic manipulators are increasingly adopted in minimal invasive surgery. However, their nonlinear behavior is challenging to model accurately, especially when subject to external interaction, potentially leading to poor control performance. In this letter, we investigate the feasibility of adopting a model-free multiagent reinforcement learning (RL), namely multiagent deep Q network (MADQN), to control a 2-degree of freedom (DoF) cable-driven continuum surgical manipulator. The control of the robot is formulated as a one-DoF, one agent problem in the MADQN framework to improve the learning efficiency. Combined with a shielding scheme that enables dynamic variation of the action set boundary, MADQN leads to efficient and importantly safer control of the robot. Shielded MADQN enabled the robot to perform point and trajectory tracking with submillimeter root mean square errors under external loads, soft obstacles, and rigid collision, which are common interaction scenarios encountered by surgical manipulators. The controller was further proven to be effective in a miniature continuum robot with high structural nonlinearitiy, achieving trajectory tracking with submillimeter accuracy under external payload.

Related papers

• Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback [16.46487826869775]
  We propose a novel framework, Neural Internal Model Control, which integrates model-based control with RL-based control to enhance robustness. Our framework streamlines the predictive model by applying Newton-Euler equations for rigid-body dynamics, eliminating the need to capture complex high-dimensional nonlinearities. We demonstrate the effectiveness of our framework on both quadrotors and quadrupedal robots, achieving superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-20T07:07:42Z)
• Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments [0.0]
  This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
  arXiv  Detail & Related papers  (2024-10-03T17:50:19Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions [9.690491406456307]
  Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. This paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data. We also proposed the Lyapunov barrier actor-critic (LBAC) to search for a controller that satisfies the data-based approximation of the safety and reachability conditions.
  arXiv  Detail & Related papers  (2023-05-16T20:27:02Z)
• Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator [56.11574814802912]
  State-of-the art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation. Model-free PID control methods are more prone to instability due to nonlinearities in the SEA. Deep reinforcement learning has proved to be an effective model-free method for continuous control tasks.
  arXiv  Detail & Related papers  (2023-04-11T00:51:47Z)
• Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC [0.0]
  We present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly, a sub-gram MAV (0.7 grams) Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC) We experimentally evaluate our approach, achieving position Root Mean Square Errors lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to our previous work, and robustness demonstrating to large external disturbances.
  arXiv  Detail & Related papers  (2022-09-20T21:30:16Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning [85.13138591433635]
  The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. In this paper, we address both challenges for the specific case of bipedal robot control by the use of reinforcement learning techniques.
  arXiv  Detail & Related papers  (2020-04-15T18:15:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.