Unsupervised state representation learning with robotic priors: a robustness benchmark

Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation of the learned representation using nearest neighbors in the state space that allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.Comment: ICRA 2018 submissio

Deep unsupervised state representation learning with robotic priors: a robustness analysis

International audienceOur understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation metric of the learned representation that uses nearest neighbors in the state space and allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset

Tissue Transglutaminase Does Not Contribute to the Formation of Mutant Huntingtin Aggregates

The cause of Huntington's disease (HD) is a pathological expansion of the polyglutamine domain within the NH2-terminal region of huntingtin. Neuronal intranuclear inclusions and cytoplasmic aggregates composed of the mutant huntingtin within certain neuronal populations are a characteristic hallmark of HD. Because in vitro expanded polyglutamine repeats are glutaminyl-donor substrates of tissue transglutaminase (tTG), it has been hypothesized that tTG may contribute to the formation of these aggregates in HD. Therefore, it is of fundamental importance to establish whether tTG plays a significant role in the formation of mutant huntingtin aggregates in the cell. Human neuroblastoma SH-SY5Y cells were stably transfected with truncated NH2-terminal huntingtin constructs containing 18 (wild type) or 82 (mutant) glutamines. In the cells expressing the mutant truncated huntingtin construct, numerous SDS-resistant aggregates were present in the cytoplasm and nucleus. Even though numerous aggregates were present in the mutant huntingtin-expressing cells, tTG did not coprecipitate with mutant truncated huntingtin. Further, tTG was totally excluded from the aggregates, and significantly increasing tTG expression had no effect on the number of aggregates or their intracellular localization (cytoplasm or nucleus). When a YFP-tagged mutant truncated huntingtin construct was transiently transfected into cells that express no detectable tTG due to stable transfection with a tTG antisense construct, there was extensive aggregate formation. These findings clearly demonstrate that tTG is not required for aggregate formation, and does not facilitate the process of aggregate formation. Therefore, in HD, as well as in other polyglutamine diseases, tTG is unlikely to play a role in the formation of aggregates

14-3-3theta Protects against Neurotoxicity in a Cellular Parkinson's Disease Model through Inhibition of the Apoptotic Factor Bax

Disruption of 14-3-3 function by alpha-synuclein has been implicated in Parkinson's disease. As 14-3-3s are important regulators of cell death pathways, disruption of 14-3-3s could result in the release of pro-apoptotic factors, such as Bax. We have previously shown that overexpression of 14-3-3θ reduces cell loss in response to rotenone and MPP+ in dopaminergic cell culture and reduces cell loss in transgenic C. elegans that overexpress alpha-synuclein. In this study, we investigate the mechanism for 14-3-3θ's neuroprotection against rotenone toxicity. While 14-3-3s can inhibit many pro-apoptotic factors, we demonstrate that inhibition of one factor in particular, Bax, is important to 14-3-3s' protection against rotenone toxicity in dopaminergic cells. We found that 14-3-3θ overexpression reduced Bax activation and downstream signaling events, including cytochrome C release and caspase 3 activation. Pharmacological inhibition or shRNA knockdown of Bax provided protection against rotenone, comparable to 14-3-3θ's neuroprotective effects. A 14-3-3θ mutant incapable of binding Bax failed to protect against rotenone. These data suggest that 14-3-3θ's neuroprotective effects against rotenone are at least partially mediated by Bax inhibition and point to a potential therapeutic role of 14-3-3s in Parkinson's disease

Chapter 8 - Multiple Roles of Glycogen Synthase Kinase-3 in Alzheimer's Disease

One of the most notable developments in research concerning Alzheimer's disease is the solid evidence directly linking glycogen synthase kinase-3 (GSK-3) to most, if not all, of the major mechanisms known to contribute to the neuropathology of the disease. GSK-3 interacts with multiple components of the plaque-producing amyloid system associated with Alzheimer's disease. GSK-3 participates in phosphorylating the microtubule-binding protein tau, which may contribute to the formation of neurofibrillary tangles in Alzheimer's disease. GSK-3 interacts with presenilin and other Alzheimer's disease-associated proteins. GSK-3 has a central role in neuronal plasticity and memory. GSK-3 promotes both inflammation and apoptosis. These many links between GSK-3 and Alzheimer's disease are discussed in this chapter

Allelic series of Huntington's disease knock-in mice reveals expression discorrelates

Identifying molecular drivers of pathology provides potential therapeutic targets. Differentiating between drivers and coincidental molecular alterations presents a major challenge. Variation unrelated to pathology further complicates transcriptomic, proteomic and metabolomic studies which measure large numbers of individual molecules. To overcome these challenges towards the goal of determining drivers of Huntington's disease (HD), we generated an allelic series of HD knock-in mice with graded levels of phenotypic severity for comparison with molecular alterations. RNA-sequencing analysis of this series reveals high numbers of transcripts with level alterations that do not correlate with phenotypic severity. These discorrelated molecular changes are unlikely to be drivers of pathology allowing an exclusion-based strategy to provide a short list of driver candidates. Further analysis of the data shows that a majority of transcript level changes in HD knock-in mice involve alteration of the rate of mRNA processing and/or degradation rather than solely being due to alteration of transcription rate. The overall strategy described can be applied to assess the influence of any molecular change on pathology for diseases where different mutations cause graded phenotypic severity.This work was supported by grants from the CHDI, Inc. (P.D., A.J.M.) and the National Institutes of Health (NS062216 to P.D., NS071168 to M.L., NS059537 to R.L.A., P30 NS47466 to T.vG., CA13148 to M.R.C. and D.K.C. and CFAR AI027767 to M.R.C and D.K.C.).This is the author accepted manuscript. The final version is available from Oxford University Press via https://doi.org/10.1093/hmg/ddw04