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

Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing

Robotic assistance presents an opportunity to benefit the lives of many people with physical disabilities, yet accurately sensing the human body and tracking human motion remain difficult for robots. We present a multidimensional capacitive sensing technique that estimates the local pose of a human limb in real time. A key benefit of this sensing method is that it can sense the limb through opaque materials, including fabrics and wet cloth. Our method uses a multielectrode capacitive sensor mounted to a robot's end effector. A neural network model estimates the position of the closest point on a person's limb and the orientation of the limb's central axis relative to the sensor's frame of reference. These pose estimates enable the robot to move its end effector with respect to the limb using feedback control. We demonstrate that a PR2 robot can use this approach with a custom six electrode capacitive sensor to assist with two activities of daily living-dressing and bathing. The robot pulled the sleeve of a hospital gown onto able-bodied participants' right arms, while tracking human motion. When assisting with bathing, the robot moved a soft wet washcloth to follow the contours of able-bodied participants' limbs, cleaning their surfaces. Overall, we found that multidimensional capacitive sensing presents a promising approach for robots to sense and track the human body during assistive tasks that require physical human-robot interaction.Comment: 8 pages, 16 figures, International Conference on Rehabilitation Robotics 201

Defect turbulence in inclined layer convection

We report experimental results on the defect turbulent state of undulation chaos in inclined layer convection of a fluid withPrandtl number $\approx 1$. By measuring defect density and undulation wavenumber, we find that the onset of undulation chaos coincides with the theoretically predicted onset for stable, stationary undulations. At stronger driving, we observe a competition between ordered undulations and undulation chaos, suggesting bistability between a fixed-point attractor and spatiotemporal chaos. In the defect turbulent regime, we measured the defect creation, annihilation, entering, leaving, and rates. We show that entering and leaving rates through boundaries must be considered in order to describe the observed statistics. We derive a universal probability distribution function which agrees with the experimental findings.Comment: 4 pages, 5 figure

Ultrafast IR spectroscopy of photo-induced electron transfer in self-assembled donor-acceptor coordination cages.

Photo-induced processes in self-assembled coordination cages were studied by femtosecond infrared pump-probe spectroscopy. Densely packed, interpenetrated double cages were constructed from eight bis-monodentate redoxactive ligands bound to four Pd(ii) nodes. Two types of ligands consisting of electron rich phenothiazine (PTZ) or electron deficient anthraquinone (ANQ) chromophores were used to assemble either homo-octameric or mixed-ligand cages. Upon photoexcitation the homo-octameric acceptor cage undergoes intersystem crossing to a long-lived triplet state, similar to the free acceptor ligand. Excitation of the free donor ligand leads to a fluorescent state with intramolecular charge transfer character. This fluorescence is completely quenched in the homo-octameric donor double cage due to a ligand-to-metal charge transfer followed by back electron transfer on a ps timescale. Only for the mixed-ligand cage irradiation produces a charge separated state with an oxidized PTZ radical cation and a reduced ANQ radical anion as proven by their vibrational fingerprints in the transient IR spectra. In dichloromethane the lifetime of this charge separated state extends from tens of ps to >1.5 ns which is attributed to the broad distribution of mixed-ligand cages with different stoichiometry and/or stereo configurations

Modeling Humans at Rest with Applications to Robot Assistance

Humans spend a large part of their lives resting. Machine perception of this class of body poses would be beneficial to numerous applications, but it is complicated by line-of-sight occlusion from bedding. Pressure sensing mats are a promising alternative, but data is challenging to collect at scale. To overcome this, we use modern physics engines to simulate bodies resting on a soft bed with a pressure sensing mat. This method can efficiently generate data at scale for training deep neural networks. We present a deep model trained on this data that infers 3D human pose and body shape from a pressure image, and show that it transfers well to real world data. We also present a model that infers pose, shape and contact pressure from a depth image facing the person in bed, and it does so in the presence of blankets. This model similarly benefits from synthetic data, which is created by simulating blankets on the bodies in bed. We evaluate this model on real world data and compare it to an existing method that requires RGB, depth, thermal and pressure imagery in the input. Our model only requires an input depth image, yet it is 12% more accurate. Our methods are relevant to applications in healthcare, including patient acuity monitoring and pressure injury prevention. We demonstrate this work in the context of robotic caregiving assistance, by using it to control a robot to move to locations on a person’s body in bed.Ph.D

Fractal Stability Border in Plane Couette Flow

We study the dynamics of localised perturbations in plane Couette flow with periodic lateral boundary conditions. For small Reynolds number and small amplitude of the initial state the perturbation decays on a viscous time scale $t \propto Re$. For Reynolds number larger than about 200, chaotic transients appear with life times longer than the viscous one. Depending on the type of the perturbation isolated initial conditions with infinite life time appear for Reynolds numbers larger than about 270--320. In this third regime, the life time as a function of Reynolds number and amplitude is fractal. These results suggest that in the transition region the turbulent dynamics is characterised by a chaotic repeller rather than an attractor.Comment: 4 pages, Latex, 4 eps-figures, submitted to Phys. Rev. Le

Localized transverse bursts in inclined layer convection

We investigate a novel bursting state in inclined layer thermal convection in which convection rolls exhibit intermittent, localized, transverse bursts. With increasing temperature difference, the bursts increase in duration and number while exhibiting a characteristic wavenumber, magnitude, and size. We propose a mechanism which describes the duration of the observed bursting intervals and compare our results to bursting processes in other systems.Comment: 4 pages, 8 figure

Asymmetric Squares as Standing Waves in Rayleigh-Benard Convection

Possibility of asymmetric square convection is investigated numerically using a few mode Lorenz-like model for thermal convection in Boussinesq fluids confined between two stress free and conducting flat boundaries. For relatively large value of Rayleigh number, the stationary rolls become unstable and asymmetric squares appear as standing waves at the onset of secondary instability. Asymmetric squares, two dimensional rolls and again asymmetric squares with their corners shifted by half a wavelength form a stable limit cycle.Comment: 8 pages, 7 figure