1,672 research outputs found
TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement
We present TOCH, a method for refining incorrect 3D hand-object interaction
sequences using a data prior. Existing hand trackers, especially those that
rely on very few cameras, often produce visually unrealistic results with
hand-object intersection or missing contacts. Although correcting such errors
requires reasoning about temporal aspects of interaction, most previous works
focus on static grasps and contacts. The core of our method are TOCH fields, a
novel spatio-temporal representation for modeling correspondences between hands
and objects during interaction. TOCH fields are a point-wise, object-centric
representation, which encode the hand position relative to the object.
Leveraging this novel representation, we learn a latent manifold of plausible
TOCH fields with a temporal denoising auto-encoder. Experiments demonstrate
that TOCH outperforms state-of-the-art 3D hand-object interaction models, which
are limited to static grasps and contacts. More importantly, our method
produces smooth interactions even before and after contact. Using a single
trained TOCH model, we quantitatively and qualitatively demonstrate its
usefulness for correcting erroneous sequences from off-the-shelf RGB/RGB-D
hand-object reconstruction methods and transferring grasps across objects
Evolutionary Dynamic Optimization and Machine Learning
Evolutionary Computation (EC) has emerged as a powerful field of Artificial
Intelligence, inspired by nature's mechanisms of gradual development. However,
EC approaches often face challenges such as stagnation, diversity loss,
computational complexity, population initialization, and premature convergence.
To overcome these limitations, researchers have integrated learning algorithms
with evolutionary techniques. This integration harnesses the valuable data
generated by EC algorithms during iterative searches, providing insights into
the search space and population dynamics. Similarly, the relationship between
evolutionary algorithms and Machine Learning (ML) is reciprocal, as EC methods
offer exceptional opportunities for optimizing complex ML tasks characterized
by noisy, inaccurate, and dynamic objective functions. These hybrid techniques,
known as Evolutionary Machine Learning (EML), have been applied at various
stages of the ML process. EC techniques play a vital role in tasks such as data
balancing, feature selection, and model training optimization. Moreover, ML
tasks often require dynamic optimization, for which Evolutionary Dynamic
Optimization (EDO) is valuable. This paper presents the first comprehensive
exploration of reciprocal integration between EDO and ML. The study aims to
stimulate interest in the evolutionary learning community and inspire
innovative contributions in this domain
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
Diffusion-based planning has shown promising results in long-horizon,
sparse-reward tasks by training trajectory diffusion models and conditioning
the sampled trajectories using auxiliary guidance functions. However, due to
their nature as generative models, diffusion models are not guaranteed to
generate feasible plans, resulting in failed execution and precluding planners
from being useful in safety-critical applications. In this work, we propose a
novel approach to refine unreliable plans generated by diffusion models by
providing refining guidance to error-prone plans. To this end, we suggest a new
metric named restoration gap for evaluating the quality of individual plans
generated by the diffusion model. A restoration gap is estimated by a gap
predictor which produces restoration gap guidance to refine a diffusion
planner. We additionally present an attribution map regularizer to prevent
adversarial refining guidance that could be generated from the sub-optimal gap
predictor, which enables further refinement of infeasible plans. We demonstrate
the effectiveness of our approach on three different benchmarks in offline
control settings that require long-horizon planning. We also illustrate that
our approach presents explainability by presenting the attribution maps of the
gap predictor and highlighting error-prone transitions, allowing for a deeper
understanding of the generated plans.Comment: NeurIPS 2023. First two authors contributed equally. Code at
http://github.com/leekwoon/rg
Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications
Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
Towards Deeper Understanding in Neuroimaging
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in feature discovery, with relevant applications to neuroimaging. Through our works within, this dissertation presents strong evidence that deep learning is a viable and important tool for neuroimaging studies
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