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Found 25 papers, 5 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.
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.
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.
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.
Point Cloud Pre-Training With Natural 3D Structures
Moreover, the PC-FractalDB pre-trained model is especially effective in training with limited data.
Ranked #18 on
3D Object Detection
on SUN-RGBD val
(using extra training data)
Emergence of sensory attenuation based upon the free-energy principle
Consequently, shifts between the two sensorimotor contexts triggered transitions from one free-energy state to another in the network via executive control, which caused shifts between attenuating and amplifying prediction-error-induced responses in the sensory areas.
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.
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
Transferable Task Execution from Pixels through Deep Planning Domain Learning
On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal, but these approaches often struggle on the real world robotic tasks due to the challenges of grounding these symbols from sensor data in a partially-observable world.
RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY
Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of train- ing driving dataset is limited (2) Lack of accident explanation ability when driving models don’t work as expected.
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments
By employing a convolutional neural network (CNN)-based multichannel end-to-end speech recognition system, this study attempts to overcome the presents difficulties in everyday environments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability
Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of training driving dataset is limited (2) Lack of accident explanation ability when driving models don't work as expected.
Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning
We confirm that the robot is capable of detecting features of tools, objects, and actions by learning the effects and executing the task.
Weakly Supervised Deep Recurrent Neural Networks for Basic Dance Step Generation
However, applying DNNs for generating dance to a piece of music is nevertheless challenging, because of 1) DNNs need to generate large sequences while mapping the music input, 2) the DNN needs to constraint the motion beat to the music, and 3) DNNs require a considerable amount of hand-crafted data.
Encoding Longer-term Contextual Multi-modal Information in a Predictive Coding Model
On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the novel or surprising signal.
AFA-PredNet: The action modulation within predictive coding
The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action.
General problem solving with category theory
Generators realize cognitive operations over a system by grouping morphisms, whilst evaluators group objects as a way to generalize outsets and goals to partially defined states.
Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models
This was done by conducting two studies based on a smaller data- set (two-dimension time sequences from non-linear functions) and a relatively large data-set (43-dimension time sequences from iCub manipulation tasks with multi-modal data).
Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs
The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot.
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people.
