121 research outputs found
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Fine manipulation tasks, such as threading cable ties or slotting a battery,
are notoriously difficult for robots because they require precision, careful
coordination of contact forces, and closed-loop visual feedback. Performing
these tasks typically requires high-end robots, accurate sensors, or careful
calibration, which can be expensive and difficult to set up. Can learning
enable low-cost and imprecise hardware to perform these fine manipulation
tasks? We present a low-cost system that performs end-to-end imitation learning
directly from real demonstrations, collected with a custom teleoperation
interface. Imitation learning, however, presents its own challenges,
particularly in high-precision domains: errors in the policy can compound over
time, and human demonstrations can be non-stationary. To address these
challenges, we develop a simple yet novel algorithm, Action Chunking with
Transformers (ACT), which learns a generative model over action sequences. ACT
allows the robot to learn 6 difficult tasks in the real world, such as opening
a translucent condiment cup and slotting a battery with 80-90% success, with
only 10 minutes worth of demonstrations. Project website:
https://tonyzhaozh.github.io/aloha
Waypoint-Based Imitation Learning for Robotic Manipulation
While imitation learning methods have seen a resurgent interest for robotic
manipulation, the well-known problem of compounding errors continues to afflict
behavioral cloning (BC). Waypoints can help address this problem by reducing
the horizon of the learning problem for BC, and thus, the errors compounded
over time. However, waypoint labeling is underspecified, and requires
additional human supervision. Can we generate waypoints automatically without
any additional human supervision? Our key insight is that if a trajectory
segment can be approximated by linear motion, the endpoints can be used as
waypoints. We propose Automatic Waypoint Extraction (AWE) for imitation
learning, a preprocessing module to decompose a demonstration into a minimal
set of waypoints which when interpolated linearly can approximate the
trajectory up to a specified error threshold. AWE can be combined with any BC
algorithm, and we find that AWE can increase the success rate of
state-of-the-art algorithms by up to 25% in simulation and by 4-28% on
real-world bimanual manipulation tasks, reducing the decision making horizon by
up to a factor of 10. Videos and code are available at
https://lucys0.github.io/awe/Comment: The first two authors contributed equall
Offline Meta-Reinforcement Learning for Industrial Insertion
Reinforcement learning (RL) can in principle let robots automatically adapt
to new tasks, but current RL methods require a large number of trials to
accomplish this. In this paper, we tackle rapid adaptation to new tasks through
the framework of meta-learning, which utilizes past tasks to learn to adapt
with a specific focus on industrial insertion tasks. Fast adaptation is crucial
because prohibitively large number of on-robot trials will potentially damage
hardware pieces. Additionally, effective adaptation is also feasible in that
experience among different insertion applications can be largely leveraged by
each other. In this setting, we address two specific challenges when applying
meta-learning. First, conventional meta-RL algorithms require lengthy online
meta-training. We show that this can be replaced with appropriately chosen
offline data, resulting in an offline meta-RL method that only requires
demonstrations and trials from each of the prior tasks, without the need to run
costly meta-RL procedures online. Second, meta-RL methods can fail to
generalize to new tasks that are too different from those seen at meta-training
time, which poses a particular challenge in industrial applications, where high
success rates are critical. We address this by combining contextual
meta-learning with direct online finetuning: if the new task is similar to
those seen in the prior data, then the contextual meta-learner adapts
immediately, and if it is too different, it gradually adapts through
finetuning. We show that our approach is able to quickly adapt to a variety of
different insertion tasks, with a success rate of 100% using only a fraction of
the samples needed for learning the tasks from scratch. Experiment videos and
details are available at
https://sites.google.com/view/offline-metarl-insertion.Comment: ICRA 202
Comparison of Non-human Primate versus Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Treatment of Myocardial Infarction.
Non-human primates (NHPs) can serve as a human-like model to study cell therapy using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). However, whether the efficacy of NHP and human iPSC-CMs is mechanistically similar remains unknown. To examine this, RNU rats received intramyocardial injection of 1 × 107 NHP or human iPSC-CMs or the same number of respective fibroblasts or PBS control (n = 9-14/group) at 4 days after 60-min coronary artery occlusion-reperfusion. Cardiac function and left ventricular remodeling were similarly improved in both iPSC-CM-treated groups. To mimic the ischemic environment in the infarcted heart, both cultured NHP and human iPSC-CMs underwent 24-hr hypoxia in vitro. Both cells and media were collected, and similarities in transcriptomic as well as metabolomic profiles were noted between both groups. In conclusion, both NHP and human iPSC-CMs confer similar cardioprotection in a rodent myocardial infarction model through relatively similar mechanisms via promotion of cell survival, angiogenesis, and inhibition of hypertrophy and fibrosis
Optimal rates of convergence for estimating Toeplitz covariance matrices,” Prob
Abstract Toeplitz covariance matrices are used in the analysis of stationary stochastic processes and a wide range of applications including radar imaging, target detection, speech recognition, and communications systems. In this paper, we consider optimal estimation of large Toeplitz covariance matrices and establish the minimax rate of convergence for two commonly used parameter spaces under the spectral norm. The properties of the tapering and banding estimators are studied in detail and are used to obtain the minimax upper bound. The results also reveal a fundamental difference between the tapering and banding estimators over certain parameter spaces. The minimax lower bound is derived through a novel construction of a more informative experiment for which the minimax lower bound is obtained through an equivalent Gaussian scale model and through a careful selection of a finite collection of least favorable parameters. In addition, optimal rate of convergence for estimating the inverse of a Toeplitz covariance matrix is also established
Global Analysis of the Impact of Environmental Perturbation on cis-Regulation of Gene Expression
Genetic variants altering cis-regulation of normal gene expression (cis-eQTLs) have been extensively mapped in human cells and tissues, but the extent by which controlled, environmental perturbation influences cis-eQTLs is unclear. We carried out large-scale induction experiments using primary human bone cells derived from unrelated donors of Swedish origin treated with 18 different stimuli (7 treatments and 2 controls, each assessed at 2 time points). The treatments with the largest impact on the transcriptome, verified on two independent expression arrays, included BMP-2 (t = 2h), dexamethasone (DEX) (t = 24h), and PGE2 (t = 24h). Using these treatments and control, we performed expression profiling for 18,144 RefSeq transcripts on biological replicates of the complete study cohort of 113 individuals (ntotal = 782) and combined it with genome-wide SNP-genotyping data in order to map treatment-specific cis-eQTLs (defined as SNPs located within the gene ±250 kb). We found that 93% of cis-eQTLs at 1% FDR were observed in at least one additional treatment, and in fact, on average, only 1.4% of the cis-eQTLs were considered as treatment-specific at high confidence. The relative invariability of cis-regulation following perturbation was reiterated independently by genome-wide allelic expression tests where only a small proportion of variance could be attributed to treatment. Treatment-specific cis-regulatory effects were, however, 2- to 6-fold more abundant among differently expressed genes upon treatment. We further followed-up and validated the DEX–specific cis-regulation of the MYO6 and TNC loci and found top cis-regulatory variants located 180 kb and 250 kb upstream of the transcription start sites, respectively. Our results suggest that, as opposed to tissue-specificity of cis-eQTLs, the interactions between cellular environment and cis-variants are relatively rare (∼1.5%), but that detection of such specific interactions can be achieved by a combination of functional genomic approaches as described here
Post transcriptional control of the epigenetic stem cell regulator PLZF by sirtuin and HDAC deacetylases
The clinical relevance of oliguria in the critically ill patient : Analysis of a large observational database
Funding Information: Marc Leone reports receiving consulting fees from Amomed and Aguettant; lecture fees from MSD, Pfizer, Octapharma, 3 M, Aspen, Orion; travel support from LFB; and grant support from PHRC IR and his institution. JLV is the Editor-in-Chief of Critical Care. The other authors declare that they have no relevant financial interests. Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Urine output is widely used as one of the criteria for the diagnosis and staging of acute renal failure, but few studies have specifically assessed the role of oliguria as a marker of acute renal failure or outcomes in general intensive care unit (ICU) patients. Using a large multinational database, we therefore evaluated the occurrence of oliguria (defined as a urine output 16 years) patients in the ICON audit who had a urine output measurement on the day of admission were included. To investigate the association between oliguria and mortality, we used a multilevel analysis. Results: Of the 8292 patients included, 2050 (24.7%) were oliguric during the first 24 h of admission. Patients with oliguria on admission who had at least one additional 24-h urine output recorded during their ICU stay (n = 1349) were divided into three groups: transient - oliguria resolved within 48 h after the admission day (n = 390 [28.9%]), prolonged - oliguria resolved > 48 h after the admission day (n = 141 [10.5%]), and permanent - oliguria persisting for the whole ICU stay or again present at the end of the ICU stay (n = 818 [60.6%]). ICU and hospital mortality rates were higher in patients with oliguria than in those without, except for patients with transient oliguria who had significantly lower mortality rates than non-oliguric patients. In multilevel analysis, the need for RRT was associated with a significantly higher risk of death (OR = 1.51 [95% CI 1.19-1.91], p = 0.001), but the presence of oliguria on admission was not (OR = 1.14 [95% CI 0.97-1.34], p = 0.103). Conclusions: Oliguria is common in ICU patients and may have a relatively benign nature if only transient. The duration of oliguria and need for RRT are associated with worse outcome.publishersversionPeer reviewe
Non-Standard Errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants
- …