113 research outputs found
SoundCam: A Dataset for Finding Humans Using Room Acoustics
A room's acoustic properties are a product of the room's geometry, the
objects within the room, and their specific positions. A room's acoustic
properties can be characterized by its impulse response (RIR) between a source
and listener location, or roughly inferred from recordings of natural signals
present in the room. Variations in the positions of objects in a room can
effect measurable changes in the room's acoustic properties, as characterized
by the RIR. Existing datasets of RIRs either do not systematically vary
positions of objects in an environment, or they consist of only simulated RIRs.
We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms
publicly released to date. It includes 5,000 10-channel real-world measurements
of room impulse responses and 2,000 10-channel recordings of music in three
different rooms, including a controlled acoustic lab, an in-the-wild living
room, and a conference room, with different humans in positions throughout each
room. We show that these measurements can be used for interesting tasks, such
as detecting and identifying humans, and tracking their positions.Comment: In NeurIPS 2023 Datasets and Benchmarks Track. Project page:
https://masonlwang.com/soundcam/. Wang and Clarke contributed equally to this
wor
Groundwater Arsenic-Attributable Cardiovascular Disease (CVD) Mortality Risks in India
From MDPI via Jisc Publications RouterHistory: accepted 2021-08-12, pub-electronic 2021-08-17Publication status: PublishedFunder: Natural Environment Research Council; Grant(s): NE/R003386/1Funder: Department of Science and Technology (India); Grant(s): DST/TM/INDO-UK/2K17/55(C) & 55(G)Cardiovascular diseases (CVDs) have been recognized as the most serious non-carcinogenic detrimental health outcome arising from chronic exposure to arsenic. Drinking arsenic contaminated groundwaters is a critical and common exposure pathway for arsenic, notably in India and other countries in the circum-Himalayan region. Notwithstanding this, there has hitherto been a dearth of data on the likely impacts of this exposure on CVD in India. In this study, CVD mortality risks arising from drinking groundwater with high arsenic (>10 μg/L) in India and its constituent states, territories, and districts were quantified using the population-attributable fraction (PAF) approach. Using a novel pseudo-contouring approach, we estimate that between 58 and 64 million people are exposed to arsenic exceeding 10 μg/L in groundwater-derived drinking water in India. On an all-India basis, we estimate that 0.3–0.6% of CVD mortality is attributable to exposure to high arsenic groundwaters, corresponding to annual avoidable premature CVD-related deaths attributable to chronic exposure to groundwater arsenic in India of between around 6500 and 13,000. Based on the reported reduction in life of 12 to 28 years per death due to heart disease, we calculate value of statistical life (VSL) based annual costs to India of arsenic-attributable CVD mortality of between USD 750 million and USD 3400 million
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
Large language models (LLMs) are shown to possess a wealth of actionable
knowledge that can be extracted for robot manipulation in the form of reasoning
and planning. Despite the progress, most still rely on pre-defined motion
primitives to carry out the physical interactions with the environment, which
remains a major bottleneck. In this work, we aim to synthesize robot
trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a
large variety of manipulation tasks given an open-set of instructions and an
open-set of objects. We achieve this by first observing that LLMs excel at
inferring affordances and constraints given a free-form language instruction.
More importantly, by leveraging their code-writing capabilities, they can
interact with a vision-language model (VLM) to compose 3D value maps to ground
the knowledge into the observation space of the agent. The composed value maps
are then used in a model-based planning framework to zero-shot synthesize
closed-loop robot trajectories with robustness to dynamic perturbations. We
further demonstrate how the proposed framework can benefit from online
experiences by efficiently learning a dynamics model for scenes that involve
contact-rich interactions. We present a large-scale study of the proposed
method in both simulated and real-robot environments, showcasing the ability to
perform a large variety of everyday manipulation tasks specified in free-form
natural language. Videos and code at https://voxposer.github.i
Primitive Skill-based Robot Learning from Human Evaluative Feedback
Reinforcement learning (RL) algorithms face significant challenges when
dealing with long-horizon robot manipulation tasks in real-world environments
due to sample inefficiency and safety issues. To overcome these challenges, we
propose a novel framework, SEED, which leverages two approaches: reinforcement
learning from human feedback (RLHF) and primitive skill-based reinforcement
learning. Both approaches are particularly effective in addressing sparse
reward issues and the complexities involved in long-horizon tasks. By combining
them, SEED reduces the human effort required in RLHF and increases safety in
training robot manipulation with RL in real-world settings. Additionally,
parameterized skills provide a clear view of the agent's high-level intentions,
allowing humans to evaluate skill choices before they are executed. This
feature makes the training process even safer and more efficient. To evaluate
the performance of SEED, we conducted extensive experiments on five
manipulation tasks with varying levels of complexity. Our results show that
SEED significantly outperforms state-of-the-art RL algorithms in sample
efficiency and safety. In addition, SEED also exhibits a substantial reduction
of human effort compared to other RLHF methods. Further details and video
results can be found at https://seediros23.github.io/
Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India
From Springer Nature via Jisc Publications RouterHistory: received 2019-12-15, accepted 2020-06-24, registration 2020-06-24, pub-electronic 2020-07-11, online 2020-07-11, pub-print 2021-07Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266; Grant(s): IAA Impact AwardAbstract: Geogenic arsenic contamination in groundwaters poses a severe health risk to hundreds of millions of people globally. Notwithstanding the particular risks to exposed populations in the Indian sub-continent, at the time of writing, there was a paucity of geostatistically based models of the spatial distribution of groundwater hazard in India. In this study, we used logistic regression models of secondary groundwater arsenic data with research-informed secondary soil, climate and topographic variables as principal predictors generate hazard and risk maps of groundwater arsenic at a resolution of 1 km across Gujarat State. By combining models based on different arsenic concentrations, we have generated a pseudo-contour map of groundwater arsenic concentrations, which indicates greater arsenic hazard (> 10 μg/L) in the northwest, northeast and south-east parts of Kachchh District as well as northwest and southwest Banas Kantha District. The total number of people living in areas in Gujarat with groundwater arsenic concentration exceeding 10 μg/L is estimated to be around 122,000, of which we estimate approximately 49,000 people consume groundwater exceeding 10 µg/L. Using simple previously published dose–response relationships, this is estimated to have given rise to 700 (prevalence) cases of skin cancer and around 10 cases of premature avoidable mortality/annum from internal (lung, liver, bladder) cancers—that latter value is on the order of just 0.001% of internal cancers in Gujarat, reflecting the relative low groundwater arsenic hazard in Gujarat State
NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
We present Neural Signal Operated Intelligent Robots (NOIR), a
general-purpose, intelligent brain-robot interface system that enables humans
to command robots to perform everyday activities through brain signals. Through
this interface, humans communicate their intended objects of interest and
actions to the robots using electroencephalography (EEG). Our novel system
demonstrates success in an expansive array of 20 challenging, everyday
household activities, including cooking, cleaning, personal care, and
entertainment. The effectiveness of the system is improved by its synergistic
integration of robot learning algorithms, allowing for NOIR to adapt to
individual users and predict their intentions. Our work enhances the way humans
interact with robots, replacing traditional channels of interaction with
direct, neural communication. Project website: https://noir-corl.github.io/
Sugarcane bagasse dietary fiber as an adjuvant therapy for stable chronic obstructive pulmonary disease: a four-center, randomized, double-blind, placebo-controlled study
AbstractObjectiveTo evaluate the efficacy and safety of sugarcane bagasse dietary fiber as an adjuvant therapy for improving quality of life in patients with stable chronic obstructive pulmonary disease (COPD).MethodsThis was a multicenter, randomized, double-blind, placebo-controlled trial. A total of 196 participants were randomized into a trial group (treated with 6 g/day sugarcane bagasse plus conventional treatment, n = 98) and a control group (treated with placebo plus conventional treatment, n = 98). All efficacy analyses were performed according to the intention-to-treat (ITT) principle. A per-protocol analysis set (PPS) was used to analyze the cases that completed the clinical trial with good compliance. The trial period was 30 days, with a 6-month follow-up. Pre- and post-treatment pulmonary symptom scores (cough, sputum, wheezing, and dyspnea) were recorded for both groups. The St. George's Respiratory Questionnaire (SGRQ) and the modified Medical Research Council (mMRC) dyspnea scale were assessed before treatment and at the end of the 6-month follow-up.ResultsThe ITT population was 178 and the PPS population was 166. Post-treatment pulmonary clinical symptoms and severity of dyspnea (mMRC and SGRQ evaluation) were significantly improved in both the trial group and the control group (ITT and PPS: P < 0.05). However, there was no statistical difference between the two groups in post-treatment pulmonary symptoms and mMRC. There was a greater reduction in the SGRQ subscales of activity, effect and total score in the trial group compared with the control group (ITT and PPS: P < 0.01). There was no statistical difference in pre- and post-treatment safety variables in either group.ConclusionSugarcane bagasse combined with conventional treatment improved quality of life in patients with stable COPD. Sugarcane bagasse appears to be a safe herbal medicine with potential for treating patients with stable COPD when taken orally as an adjuvant therapy
Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear
Developing embodied agents in simulation has been a key research topic in
recent years. Exciting new tasks, algorithms, and benchmarks have been
developed in various simulators. However, most of them assume deaf agents in
silent environments, while we humans perceive the world with multiple senses.
We introduce Sonicverse, a multisensory simulation platform with integrated
audio-visual simulation for training household agents that can both see and
hear. Sonicverse models realistic continuous audio rendering in 3D environments
in real-time. Together with a new audio-visual VR interface that allows humans
to interact with agents with audio, Sonicverse enables a series of embodied AI
tasks that need audio-visual perception. For semantic audio-visual navigation
in particular, we also propose a new multi-task learning model that achieves
state-of-the-art performance. In addition, we demonstrate Sonicverse's realism
via sim-to-real transfer, which has not been achieved by other simulators: an
agent trained in Sonicverse can successfully perform audio-visual navigation in
real-world environments. Sonicverse is available at:
https://github.com/StanfordVL/Sonicverse.Comment: In ICRA 2023. Project page:
https://ai.stanford.edu/~rhgao/sonicverse/. Code:
https://github.com/StanfordVL/sonicverse. Gao and Li contributed equally to
this work and are in alphabetical orde
Differentiable Physics Simulation of Dynamics-Augmented Neural Objects
We present a differentiable pipeline for simulating the motion of objects
that represent their geometry as a continuous density field parameterized as a
deep network. This includes Neural Radiance Fields (NeRFs), and other related
models. From the density field, we estimate the dynamical properties of the
object, including its mass, center of mass, and inertia matrix. We then
introduce a differentiable contact model based on the density field for
computing normal and friction forces resulting from collisions. This allows a
robot to autonomously build object models that are visually and
\emph{dynamically} accurate from still images and videos of objects in motion.
The resulting Dynamics-Augmented Neural Objects (DANOs) are simulated with an
existing differentiable simulation engine, Dojo, interacting with other
standard simulation objects, such as spheres, planes, and robots specified as
URDFs. A robot can use this simulation to optimize grasps and manipulation
trajectories of neural objects, or to improve the neural object models through
gradient-based real-to-simulation transfer. We demonstrate the pipeline to
learn the coefficient of friction of a bar of soap from a real video of the
soap sliding on a table. We also learn the coefficient of friction and mass of
a Stanford bunny through interactions with a Panda robot arm from synthetic
data, and we optimize trajectories in simulation for the Panda arm to push the
bunny to a goal location
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