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Found 9 papers, 2 papers with code
A Peg-in-hole Task Strategy for Holes in Concrete
Since the displacement increases as the peg gets closer to the hole (due to the chamfered shape of holes in concrete), it is a useful parameter for inputting in the DNN.
Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions
In this study, we introduce a vision and proprioceptive data-driven robot control model for this task that is robust to challenging lighting and hole surface conditions.
A generative framework for conversational laughter: Its 'language model' and laughter sound synthesis
As the phonetic and acoustic manifestations of laughter in conversation are highly diverse, laughter synthesis should be capable of accommodating such diversity while maintaining high controllability.
Deep Active Visual Attention for Real-time Robot Motion Generation: Emergence of Tool-body Assimilation and Adaptive Tool-use
The model incorporates a state-driven active top-down visual attention module, which acquires attentions that can actively change targets based on task states.
How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning
We also examine the contributions of images, force, and tactile data and show that learning a variety of multimodal information results in rich perception for tool use.
Embodying Pre-Trained Word Embeddings Through Robot Actions
These embeddings allow the robot to properly generate actions from unseen words that are not paired with actions in a dataset.
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning
However, a manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance.
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis
In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series.
Accuracy of Automatic Cross-Corpus Emotion Labeling for Conversational Speech Corpus Commonization
There exists a major incompatibility in emotion labeling framework among emotional speech corpora, that is, category-based and dimension-based.
