550 research outputs found
Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction
Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use learning to model the kinematics of the task, and for autonomous execution the robot then relies on a stiff position controller. While many tasks can and have been learned this way, there are tasks in which controlling the position alone is insufficient to achieve the goals of the task. These are typically tasks that involve contact or require a specific response to physical perturbations. The question of how to adjust the compliance to suit the need of the task has not yet been fully treated in Robot Learning from Demonstration. In this paper, we address this issue and present interfaces that allow a human teacher to indicate compliance variations by physically interacting with the robot during task execution. We validate our approach in two different experiments on the 7 DoF Barrett WAM and KUKA LWR robot manipulators. Furthermore, we conduct a user study to evaluate the usability of our approach from a non-roboticists perspective
Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning
There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered.This research was funded by the University of Málaga, the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, grant number RTI2018-093421-B-I00 and the European Commission, grant number BES-2016-078237. Partial funding for open access charge: Universidad de Málag
Haptics in Robot-Assisted Surgery: Challenges and Benefits
Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man-machine collaborative type, surgical robots are seen as media that transfer pre- and intra-operative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper objectives and challenges of deploying haptic technologies in surgical robotics is discussed and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It has been tried to encompass both classical works and the state of the art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts
Tactile Sensing for Robotic Applications
This chapter provides an overview of tactile sensing in robotics. This chapter is an attempt
to answer three basic questions:
\u2022 What is meant by Tactile Sensing?
\u2022 Why Tactile Sensing is important?
\u2022 How Tactile Sensing is achieved?
The chapter is organized to sequentially provide the answers to above basic questions.
Tactile sensing has often been considered as force sensing, which is not wholly true. In order
to clarify such misconceptions about tactile sensing, it is defined in section 2. Why tactile
section is important for robotics and what parameters are needed to be measured by tactile
sensors to successfully perform various tasks, are discussed in section 3. An overview of
`How tactile sensing has been achieved\u2019 is given in section 4, where a number of
technologies and transduction methods, that have been used to improve the tactile sensing
capability of robotic devices, are discussed. Lack of any tactile analog to Complementary
Metal Oxide Semiconductor (CMOS) or Charge Coupled Devices (CCD) optical arrays has
often been cited as one of the reasons for the slow development of tactile sensing vis-\ue0-vis
other sense modalities like vision sensing. Our own contribution \u2013 development of tactile
sensing arrays using piezoelectric polymers and involving silicon micromachining - is an
attempt in the direction of achieving tactile analog of CMOS optical arrays. The first phase
implementation of these tactile sensing arrays is discussed in section 5. Section 6 concludes
the chapter with a brief discussion on the present status of tactile sensing and the challenges
that remain to be solved
Model-free Probabilistic Movement Primitives for physical interaction
Physical interaction in robotics is a complex problem
that requires not only accurate reproduction of the kinematic
trajectories but also of the forces and torques exhibited
during the movement. We base our approach on Movement
Primitives (MP), as MPs provide a framework for modelling
complex movements and introduce useful operations on the
movements, such as generalization to novel situations, time
scaling, and others. Usually, MPs are trained with imitation
learning, where an expert demonstrates the trajectories. However,
MPs used in physical interaction either require additional
learning approaches, e.g., reinforcement learning, or are based
on handcrafted solutions. Our goal is to learn and generate
movements for physical interaction that are learned with imitation
learning, from a small set of demonstrated trajectories.
The Probabilistic Movement Primitives (ProMPs) framework
is a recent MP approach that introduces beneficial properties,
such as combination and blending of MPs, and represents the
correlations present in the movement. The ProMPs provides
a variable stiffness controller that reproduces the movement
but it requires a dynamics model of the system. Learning such
a model is not a trivial task, and, therefore, we introduce the
model-free ProMPs, that are learning jointly the movement and
the necessary actions from a few demonstrations. We derive
a variable stiffness controller analytically. We further extent
the ProMPs to include force and torque signals, necessary for
physical interaction. We evaluate our approach in simulated
and real robot tasks
ILoSA: Interactive Learning of Stiffness and Attractors
Teaching robots how to apply forces according to our preferences is still an
open challenge that has to be tackled from multiple engineering perspectives.
This paper studies how to learn variable impedance policies where both the
Cartesian stiffness and the attractor can be learned from human demonstrations
and corrections with a user-friendly interface. The presented framework, named
ILoSA, uses Gaussian Processes for policy learning, identifying regions of
uncertainty and allowing interactive corrections, stiffness modulation and
active disturbance rejection. The experimental evaluation of the framework is
carried out on a Franka-Emika Panda in three separate cases with unique force
interaction properties: 1) pulling a plug wherein a sudden force discontinuity
occurs upon successful removal of the plug, 2) pushing a box where a sustained
force is required to keep the robot in motion, and 3) wiping a whiteboard in
which the force is applied perpendicular to the direction of movement
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