Optimal action sequence generation for assistive agents in fixed horizon tasks

2020 ◽  
Vol 34 (2) ◽  
Author(s):  
Kim Baraka ◽  
Francisco S. Melo ◽  
Marta Couto ◽  
Manuela Veloso
Keyword(s):  
Action Sequence ◽  
Sequence Generation ◽  
Optimal Action

scholarly journals Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

2020 ◽  
Vol 34 (07) ◽  
pp. 12281-12288
Author(s):  
Zhenyi Wang ◽  
Ping Yu ◽  
Yang Zhao ◽  
Ruiyi Zhang ◽  
Yufan Zhou ◽  
...  
Keyword(s):  
Human Action ◽  
Human Motion ◽  
Motion Generation ◽  
Action Sequence ◽  
Sequence Generation ◽  
Latent Space ◽  
Action Sequences ◽  
Mixed Classes ◽  
Standard Action ◽  
Net Framework

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which can not only generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.

scholarly journals The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

2021 ◽  
Author(s):  
Rachel M. Brown ◽  
Erik Friedgen ◽  
Iring Koch
Keyword(s):  
Action Planning ◽  
Spatial Effects ◽  
Motor Sequence ◽  
Action Sequence ◽  
Sequential Action ◽  
Spatial Features ◽  
Temporal Features ◽  
Action Sequences ◽  
Temporal And Spatial ◽  
Anticipation Effect

AbstractActions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

Reinforcement Learning in Latent Action Sequence Space

2020 ◽  
Author(s):  
Heecheol Kim ◽  
Masanori Yamada ◽  
Kosuke Miyoshi ◽  
Tomoharu Iwata ◽  
Hiroshi Yamakawa
Keyword(s):  
Reinforcement Learning ◽  
Sequence Space ◽  
Action Sequence
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