On robot compliance. A cerebellar control approach

• URL: http://arxiv.org/abs/2003.01033v2
• Date: Tue, 31 Mar 2020 07:19:55 GMT
• Title: On robot compliance. A cerebellar control approach
• Authors: Ignacio Abadia, Francisco Naveros, Jesus A. Garrido, Eduardo Ros, Niceto R. Luque
• Abstract summary: The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT) We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bio inspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

Related papers

• Unifying 3D Representation and Control of Diverse Robots with a Single Camera [48.279199537720714]
  We introduce Neural Jacobian Fields, an architecture that autonomously learns to model and control robots from vision alone. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System [5.497832119577795]
  dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors. Learning from Demonstrations offers an intuitive alternative, allowing robots to learn manipulations through observed actions.
  arXiv  Detail & Related papers  (2024-06-21T09:03:37Z)
• Agile and versatile bipedal robot tracking control through reinforcement learning [12.831810518025309]
  This paper proposes a versatile controller for bipedal robots. It achieves ankle and body trajectory tracking across a wide range of gaits using a single small-scale neural network. Highly flexible gait control can be achieved by combining minimal control units with high-level policy.
  arXiv  Detail & Related papers  (2024-04-12T05:25:03Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• 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)
• Agile Maneuvers in Legged Robots: a Predictive Control Approach [20.55884151818753]
  We present a contact-phase predictive and state-feedback controllers that enables legged robots to plan and perform agile locomotion skills. Our work is the first to show that predictive control can handle actuation limits, generate agile locomotion maneuvers and execute locally optimal feedback policies on hardware without the use of a separate whole-body controller.
  arXiv  Detail & Related papers  (2022-03-14T23:32:17Z)
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
  We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
  arXiv  Detail & Related papers  (2021-03-26T07:14:01Z)
• Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion [95.1825179206694]
  We present a framework that synthesizes robust controllers for a quadruped robot. A high-level controller learns to choose from a set of primitives in response to changes in the environment. A low-level controller that utilizes an established control method to robustly execute the primitives.
  arXiv  Detail & Related papers  (2020-09-21T16:49:26Z)
• Closed-loop spiking control on a neuromorphic processor implemented on the iCub [4.1388807795505365]
  We present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware. The network performs a proportional control action by encoding target, feedback, and error signals. We optimize the network structure to make it more robust to noisy inputs and device mismatch.
  arXiv  Detail & Related papers  (2020-09-01T14:17:48Z)
• Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped [64.64924554743982]
  We present a framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
  arXiv  Detail & Related papers  (2020-04-09T14:32:49Z)

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.