6,015 research outputs found
Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm
In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot’s body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on
Robotics and Automation (ICRA
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
Automating precision subtasks such as debridement (removing dead or diseased
tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci
Research Kit (dVRK) is challenging due to inherent non-linearities in
cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine
calibration method. In Phase I (coarse), we place a red calibration marker on
the end effector and let it randomly move through a set of open-loop
trajectories to obtain a large sample set of camera pixels and internal robot
end-effector configurations. This coarse data is then used to train a Deep
Neural Network (DNN) to learn the coarse transformation bias. In Phase II
(fine), the bias from Phase I is applied to move the end-effector toward a
small set of specific target points on a printed sheet. For each target, a
human operator manually adjusts the end-effector position by direct contact
(not through teleoperation) and the residual compensation bias is recorded.
This fine data is then used to train a Random Forest (RF) to learn the fine
transformation bias. Subsequent experiments suggest that without calibration,
position errors average 4.55mm. Phase I can reduce average error to 2.14mm and
the combination of Phase I and Phase II can reduces average error to 1.08mm. We
apply these results to debridement of raisins and pumpkin seeds as fragment
phantoms. Using an endoscopic stereo camera with standard edge detection,
experiments with 120 trials achieved average success rates of 94.5%, exceeding
prior results with much larger fragments (89.4%) and achieving a speedup of
2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source
code, data, and videos are available at
https://sites.google.com/view/calib-icra/.Comment: Code, data, and videos are available at
https://sites.google.com/view/calib-icra/. Final version for ICRA 201
Human-like arm motion generation: a review
In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
- …