Results 51 to 60 of about 184,437 (258)
Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains
Advanced Robotics Research, EarlyView.How are quadrupedal robots empowered to execute complex navigation tasks, including multigait and bipedal motions? Challenges in stability and real‐world adaptation persist, especially with uneven terrains and disturbances. This article presents an imitation learning framework that enhances adaptability and robustness by incorporating long short‐term ...
Erdong Xiao +3 more
wiley +1 more source
Frontiers in Computational Neuroscience, 2010 A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic
Rajesh P N Rao
doaj +1 more source
Mean-Variance Optimization in Markov Decision Processes [PDF]
, 2011 We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomized or history-based policies can improve performance.
Mannor, Shie, Tsitsiklis, John
core +3 more sources
Advanced Robotics Research, EarlyView.Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
Value-Function Approximations for Partially Observable Markov Decision Processes
, 2011 Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of ...
Hauskrecht, M.
core +1 more source
Multi-model Markov decision processes
IISE Transactions, 2021 Markov decision processes (MDPs) have found success in many application areas that involve sequential decision making under uncertainty, including the evaluation and design of treatment and screening protocols for medical decision making. However, the data used to parameterize the model can influence what policies are recommended, and multiple ...
Lauren N. Steimle +2 more
openaire +1 more source
Advanced Robotics Research, EarlyView.This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source
Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes
Energies, 2020 Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its ...
Alberto Reyes +3 more
doaj +1 more source
PReMo : An Analyzer for P robabilistic Re cursive Mo dels [PDF]
, 2007 This paper describes PReMo, a tool for analyzing Recursive Markov Chains, and their controlled/game extensions: (1-exit) Recursive Markov Decision Processes and Recursive Simple Stochastic ...
Etessami, Kousha, Wojtczak, Dominik
core +1 more source
Advanced Science, EarlyView.This study performs pan‐viromic profiling of 14,529 samples from 5,710 domestic herbivores across five Chinese provinces, establishing the DhCN‐Virome (1,085,360 viral metagenomes). It reveals species/sample‐specific viromic signatures and cross‐species transmission dynamics, aiding unified disease control.
Yue Sun +19 more
wiley +1 more source