21 research outputs found
Learning Contact-Rich Manipulation Skills with Guided Policy Search
Autonomous learning of object manipulation skills can enable robots to
acquire rich behavioral repertoires that scale to the variety of objects found
in the real world. However, current motion skill learning methods typically
restrict the behavior to a compact, low-dimensional representation, limiting
its expressiveness and generality. In this paper, we extend a recently
developed policy search method \cite{la-lnnpg-14} and use it to learn a range
of dynamic manipulation behaviors with highly general policy representations,
without using known models or example demonstrations. Our approach learns a set
of trajectories for the desired motion skill by using iteratively refitted
time-varying linear models, and then unifies these trajectories into a single
control policy that can generalize to new situations. To enable this method to
run on a real robot, we introduce several improvements that reduce the sample
count and automate parameter selection. We show that our method can acquire
fast, fluent behaviors after only minutes of interaction time, and can learn
robust controllers for complex tasks, including putting together a toy
airplane, stacking tight-fitting lego blocks, placing wooden rings onto
tight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle caps
onto bottles
H-GAP: Humanoid Control with a Generalist Planner
Humanoid control is an important research challenge offering avenues for
integration into human-centric infrastructures and enabling physics-driven
humanoid animations. The daunting challenges in this field stem from the
difficulty of optimizing in high-dimensional action spaces and the instability
introduced by the bipedal morphology of humanoids. However, the extensive
collection of human motion-captured data and the derived datasets of humanoid
trajectories, such as MoCapAct, paves the way to tackle these challenges. In
this context, we present Humanoid Generalist Autoencoding Planner (H-GAP), a
state-action trajectory generative model trained on humanoid trajectories
derived from human motion-captured data, capable of adeptly handling downstream
control tasks with Model Predictive Control (MPC). For 56 degrees of freedom
humanoid, we empirically demonstrate that H-GAP learns to represent and
generate a wide range of motor behaviours. Further, without any learning from
online interactions, it can also flexibly transfer these behaviors to solve
novel downstream control tasks via planning. Notably, H-GAP excels established
MPC baselines that have access to the ground truth dynamics model, and is
superior or comparable to offline RL methods trained for individual tasks.
Finally, we do a series of empirical studies on the scaling properties of
H-GAP, showing the potential for performance gains via additional data but not
computing. Code and videos are available at
https://ycxuyingchen.github.io/hgap/.Comment: 18 pages including appendix, 4 figure
TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation
Effective use of camera-based vision systems is essential for robust
performance in autonomous off-road driving, particularly in the high-speed
regime. Despite success in structured, on-road settings, current end-to-end
approaches for scene prediction have yet to be successfully adapted for complex
outdoor terrain. To this end, we present TerrainNet, a vision-based terrain
perception system for semantic and geometric terrain prediction for aggressive,
off-road navigation. The approach relies on several key insights and practical
considerations for achieving reliable terrain modeling. The network includes a
multi-headed output representation to capture fine- and coarse-grained terrain
features necessary for estimating traversability. Accurate depth estimation is
achieved using self-supervised depth completion with multi-view RGB and stereo
inputs. Requirements for real-time performance and fast inference speeds are
met using efficient, learned image feature projections. Furthermore, the model
is trained on a large-scale, real-world off-road dataset collected across a
variety of diverse outdoor environments. We show how TerrainNet can also be
used for costmap prediction and provide a detailed framework for integration
into a planning module. We demonstrate the performance of TerrainNet through
extensive comparison to current state-of-the-art baselines for camera-only
scene prediction. Finally, we showcase the effectiveness of integrating
TerrainNet within a complete autonomous-driving stack by conducting a
real-world vehicle test in a challenging off-road scenario
Heme Oxygenase-1 Accelerates Cutaneous Wound Healing in Mice
Heme oxygenase-1 (HO-1), a cytoprotective, pro-angiogenic and anti-inflammatory enzyme, is strongly induced in injured tissues. Our aim was to clarify its role in cutaneous wound healing. In wild type mice, maximal expression of HO-1 in the skin was observed on the 2nd and 3rd days after wounding. Inhibition of HO-1 by tin protoporphyrin-IX resulted in retardation of wound closure. Healing was also delayed in HO-1 deficient mice, where lack of HO-1 could lead to complete suppression of reepithelialization and to formation of extensive skin lesions, accompanied by impaired neovascularization. Experiments performed in transgenic mice bearing HO-1 under control of keratin 14 promoter showed that increased level of HO-1 in keratinocytes is enough to improve the neovascularization and hasten the closure of wounds. Importantly, induction of HO-1 in wounded skin was relatively weak and delayed in diabetic (db/db) mice, in which also angiogenesis and wound closure were impaired. In such animals local delivery of HO-1 transgene using adenoviral vectors accelerated the wound healing and increased the vascularization. In summary, induction of HO-1 is necessary for efficient wound closure and neovascularization. Impaired wound healing in diabetic mice may be associated with delayed HO-1 upregulation and can be improved by HO-1 gene transfer
Electronic cigarettes for smoking cessation
Background Electronic cigarettes (ECs) are handheld electronic vaping devices which produce an aerosol formed by heating an e‐liquid. Some people who smoke use ECs to stop or reduce smoking, but some organizations, advocacy groups and policymakers have discouraged this, citing lack of evidence of efficacy and safety. People who smoke, healthcare providers and regulators want to know if ECs can help people quit and if they are safe to use for this purpose. This is an update of a review first published in 2014. Objectives To examine the effectiveness, tolerability, and safety of using electronic cigarettes (ECs) to help people who smoke achieve long‐term smoking abstinence. Search methods We searched the Cochrane Tobacco Addiction Group's Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and PsycINFO to 1 February 2021, together with reference‐checking and contact with study authors. Selection criteria We included randomized controlled trials (RCTs) and randomized cross‐over trials in which people who smoke were randomized to an EC or control condition. We also included uncontrolled intervention studies in which all participants received an EC intervention. To be included, studies had to report abstinence from cigarettes at six months or longer and/or data on adverse events (AEs) or other markers of safety at one week or longer. Data collection and analysis We followed standard Cochrane methods for screening and data extraction. Our primary outcome measures were abstinence from smoking after at least six months follow‐up, adverse events (AEs), and serious adverse events (SAEs). Secondary outcomes included changes in carbon monoxide, blood pressure, heart rate, blood oxygen saturation, lung function, and levels of known carcinogens/toxicants. We used a fixed‐effect Mantel‐Haenszel model to calculate the risk ratio (RR) with a 95% confidence interval (CI) for dichotomous outcomes. For continuous outcomes, we calculated mean differences. Where appropriate, we pooled data from these studies in meta‐analyses. Main results We included 56 completed studies, representing 12,804 participants, of which 29 were RCTs. Six of the 56 included studies were new to this review update. Of the included studies, we rated five (all contributing to our main comparisons) at low risk of bias overall, 41 at high risk overall (including the 25 non‐randomized studies), and the remainder at unclear risk. There was moderate‐certainty evidence, limited by imprecision, that quit rates were higher in people randomized to nicotine EC than in those randomized to nicotine replacement therapy (NRT) (risk ratio (RR) 1.69, 95% confidence interval (CI) 1.25 to 2.27; I2 = 0%; 3 studies, 1498 participants). In absolute terms, this might translate to an additional four successful quitters per 100 (95% CI 2 to 8). There was low‐certainty evidence (limited by very serious imprecision) that the rate of occurrence of AEs was similar) (RR 0.98, 95% CI 0.80 to 1.19; I2 = 0%; 2 studies, 485 participants). SAEs occurred rarely, with no evidence that their frequency differed between nicotine EC and NRT, but very serious imprecision led to low certainty in this finding (RR 1.37, 95% CI 0.77 to 2.41: I2 = n/a; 2 studies, 727 participants). There was moderate‐certainty evidence, again limited by imprecision, that quit rates were higher in people randomized to nicotine EC than to non‐nicotine EC (RR 1.70, 95% CI 1.03 to 2.81; I2 = 0%; 4 studies, 1057 participants). In absolute terms, this might again lead to an additional four successful quitters per 100 (95% CI 0 to 11). These trials mainly used older EC with relatively low nicotine delivery. There was moderate‐certainty evidence of no difference in the rate of AEs between these groups (RR 1.01, 95% CI 0.91 to 1.11; I2 = 0%; 3 studies, 601 participants). There was insufficient evidence to determine whether rates of SAEs differed between groups, due to very serious imprecision (RR 0.60, 95% CI 0.15 to 2.44; I2 = n/a; 4 studies, 494 participants). Compared to behavioral support only/no support, quit rates were higher for participants randomized to nicotine EC (RR 2.70, 95% CI 1.39 to 5.26; I2 = 0%; 5 studies, 2561 participants). In absolute terms this represents an increase of seven per 100 (95% CI 2 to 17). However, this finding was of very low certainty, due to issues with imprecision and risk of bias. There was no evidence that the rate of SAEs differed, but some evidence that non‐serious AEs were more common in people randomized to nicotine EC (AEs: RR 1.22, 95% CI 1.12 to 1.32; I2 = 41%, low certainty; 4 studies, 765 participants; SAEs: RR 1.17, 95% CI 0.33 to 4.09; I2 = 5%; 6 studies, 1011 participants, very low certainty). Data from non‐randomized studies were consistent with RCT data. The most commonly reported AEs were throat/mouth irritation, headache, cough, and nausea, which tended to dissipate with continued use. Very few studies reported data on other outcomes or comparisons and hence evidence for these is limited, with confidence intervals often encompassing clinically significant harm and benefit. Authors' conclusions There is moderate‐certainty evidence that ECs with nicotine increase quit rates compared to ECs without nicotine and compared to NRT. Evidence comparing nicotine EC with usual care/no treatment also suggests benefit, but is less certain. More studies are needed to confirm the size of effect, particularly when using modern EC products. Confidence intervals were for the most part wide for data on AEs, SAEs and other safety markers, though evidence indicated no difference in AEs between nicotine and non‐nicotine ECs. Overall incidence of SAEs was low across all study arms. We did not detect any clear evidence of harm from nicotine EC, but longest follow‐up was two years and the overall number of studies was small. The evidence is limited mainly by imprecision due to the small number of RCTs, often with low event rates. Further RCTs are underway. To ensure the review continues to provide up‐to‐date information, this review is now a living systematic review. We run searches monthly, with the review updated when relevant new evidence becomes available. Please refer to the Cochrane Database of Systematic Reviews for the review's current status