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Found 10 papers, 2 papers with code
Realtime Motion Generation with Active Perception Using Attention Mechanism for Cooking Robot
We tackled on the task of cooking scrambled eggs using real ingredients, in which the robot needs to perceive the states of the egg and adjust stirring movement in real time, while the egg is heated and the state changes continuously.
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model
The application of the Large Language Model (LLM) to robot action planning has been actively studied.
Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data
We experimentally evaluated our method using a paired dataset consisting of motion-captured actions and descriptions.
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available.
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.
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask
The learning instability caused by these unstable signals is a problem that remains to be solved in DRL.
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation
Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine.
Action Generation
Cultural Vocal Bursts Intensity Prediction
+1
A Multi-task Learning Framework for Grasping-Position Detection and Few-Shot Classification
However, the DNN that was used to detect grasping positions has two problems with respect to extracting feature vectors from a layer for shape classification: (1) Because each layer of the grasping position detection DNN is activated by all objects in the input image, it is necessary to refine the features for each grasping position.
Robotics
Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach
: SSD that detects the grasping position of an object, and Siamese networks (SNs) that evaluate the trial sample using the similarity of two input data in the feature space.
