Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization

• URL: http://arxiv.org/abs/2312.05023v1
• Date: Fri, 8 Dec 2023 13:04:41 GMT
• Title: Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization
• Authors: Hirakjyoti Basumatary, Daksh Adhar, Atharva Shrawge, Prathamesh Kanbaskar and Shyamanta M. Hazarika
• Abstract summary: We introduce an innovative bionic reflex control pipeline, leveraging reinforcement learning (RL) Our proposed bionic reflex controller has been designed and tested on an anthropomorphic hand. We anticipate that this autonomous, RL-based bionic reflex controller will catalyze the development of dependable and highly efficient robotic and prosthetic hands.
• Score: 0.4999814847776098
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Achieving human-level dexterity in robotic grasping remains a challenging endeavor. Robotic hands frequently encounter slippage and deformation during object manipulation, issues rarely encountered by humans due to their sensory receptors, experiential learning, and motor memory. The emulation of the human grasping reflex within robotic hands is referred to as the ``bionic reflex". Past endeavors in the realm of bionic reflex control predominantly relied on model-based and supervised learning approaches, necessitating human intervention during thresholding and labeling tasks. In this study, we introduce an innovative bionic reflex control pipeline, leveraging reinforcement learning (RL); thereby eliminating the need for human intervention during control design. Our proposed bionic reflex controller has been designed and tested on an anthropomorphic hand, manipulating deformable objects in the PyBullet physics simulator, incorporating domain randomization (DR) for enhanced Sim2Real transferability. Our findings underscore the promise of RL as a potent tool for advancing bionic reflex control within anthropomorphic robotic hands. We anticipate that this autonomous, RL-based bionic reflex controller will catalyze the development of dependable and highly efficient robotic and prosthetic hands, revolutionizing human-robot interaction and assistive technologies.

Related papers

• Naturalistic Robot Arm Trajectory Generation via Representation Learning [4.7682079066346565]
  Integration of manipulator robots in household environments suggests a need for more predictable human-like robot motion. One method of generating naturalistic motion trajectories is via imitation of human demonstrators. This paper explores a self-supervised imitation learning method using an autoregressive neural network for an assistive drinking task.
  arXiv  Detail & Related papers  (2023-09-14T09:26:03Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
  We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
  arXiv  Detail & Related papers  (2022-12-08T15:56:13Z)
• 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)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• From Biological Synapses to Intelligent Robots [0.0]
  Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence. The potential for adaptive learning and control without supervision is brought forward. The insights collected here point toward the Hebbian model as a choice solution for intelligent robotics and sensor systems.
  arXiv  Detail & Related papers  (2022-02-25T12:39:22Z)
• I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation [5.066245628617513]
  Functional Electrical Stimulation (FES) is an established technique for contracting muscles by stimulating the skin above a muscle to induce its contraction. We present our Deep Reinforcement Learning approach to the control of human muscles with FES, using a recurrent neural network for dynamic state representation. Our results show that our controller can learn to manipulate human muscles, applying appropriate levels of stimulation to achieve the given tasks while compensating for advancing muscle fatigue which arises throughout the tasks.
  arXiv  Detail & Related papers  (2021-03-09T10:58:51Z)
• Sensorimotor representation learning for an "active self" in robots: A model survey [10.649413494649293]
  In humans, these capabilities are thought to be related to our ability to perceive our body in space. This paper reviews the developmental processes of underlying mechanisms of these abilities. We propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents.
  arXiv  Detail & Related papers  (2020-11-25T16:31:01Z)
• 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.