What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Marc Toussaint spends much of his time researching Artificial intelligence, Mathematical optimization, Machine learning, Inference and Robot.
His work deals with themes such as Algorithm and Trajectory, which intersect with Artificial intelligence.
His study in Algorithm is interdisciplinary in nature, drawing from both Humanoid robot and Statistical model.
His Mathematical optimization research integrates issues from Partially observable Markov decision process and Dynamic Bayesian network.
Marc Toussaint works mostly in the field of Machine learning, limiting it down to concerns involving Robustness and, occasionally, Bayesian probability and Upper and lower bounds.
His biological study spans a wide range of topics, including Probabilistic logic and Leverage.
His most cited work include:
- Probabilistic inference for solving discrete and continuous state Markov Decision Processes (455 citations)
- Using Machine Learning to Focus Iterative Optimization (354 citations)
- Robot trajectory optimization using approximate inference (233 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Robot, Mathematical optimization, Machine learning and Motion planning.
His Artificial intelligence course of study focuses on Computer vision and GRASP.
The Robot study combines topics in areas such as Motion, Leverage, Degrees of freedom, Human–computer interaction and Trajectory.
His work carried out in the field of Mathematical optimization brings together such families of science as Partially observable Markov decision process and Approximate inference, Inference.
The study incorporates disciplines such as Generalization and Inverse dynamics in addition to Machine learning.
His Motion planning research is multidisciplinary, incorporating elements of Tree, Algorithm, Morse theory and Maxima and minima.
He most often published in these fields:
- Artificial intelligence (49.79%)
- Robot (26.75%)
- Mathematical optimization (19.75%)
What were the highlights of his more recent work (between 2018-2021)?
- Motion planning (17.70%)
- Robot (26.75%)
- Artificial intelligence (49.79%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Motion planning, Robot, Artificial intelligence, Algorithm and Trajectory optimization.
Marc Toussaint has included themes like Tree, Morse theory, Mathematical optimization and Maxima and minima in his Motion planning study.
His studies deal with areas such as Object, Degrees of freedom, Human–computer interaction and GRASP as well as Robot.
His Artificial intelligence research incorporates elements of Machine learning, Computer vision and Nonlinear system.
His specific area of interest is Machine learning, where he studies Reinforcement learning.
Marc Toussaint combines subjects such as Upper and lower bounds and Configuration space with his study of Algorithm.
Between 2018 and 2021, his most popular works were:
- Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image (12 citations)
- Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Manipulation Planning (11 citations)
- Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
The scientist’s investigation covers issues in Robot, Motion planning, Artificial intelligence, Trajectory optimization and Algorithm.
His Robot study combines topics in areas such as Solver and Leverage.
His research in Motion planning intersects with topics in Tree and Mathematical optimization.
His Artificial intelligence research includes elements of Machine learning and Computer vision.
His biological study deals with issues like Motion capture, which deal with fields such as GRASP and Trajectory.
His work focuses on many connections between Trajectory optimization and other disciplines, such as Recurrent neural network, that overlap with his field of interest in Encoding and Motion.
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