1 |
From Biological Synapses to “Intelligent” Robots
|
|
|
|
In: ISSN: 2079-9292 ; Electronics ; https://hal.archives-ouvertes.fr/hal-03590998 ; Electronics, MDPI, 2022, 11 (5), pp.707. ⟨10.3390/electronics11050707⟩ (2022)
|
|
BASE
|
|
Show details
|
|
2 |
Cross-Situational Learning Towards Robot Grounding
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Cross-Situational Learning Towards Robot Grounding
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
|
|
BASE
|
|
Show details
|
|
4 |
Finding the best way to put media bias research into practice via an annotation app ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Are neural language models sensitive to false belief? A computational study. ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Can distributional semantics explain performance on the false belief task? ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following ...
|
|
|
|
Abstract:
This paper investigates robot manipulation based on human instruction with ambiguous requests. The intent is to compensate for imperfect natural language via visual observations. Early symbolic methods, based on manually defined symbols, built modular framework consist of semantic parsing and task planning for producing sequences of actions from natural language requests. Modern connectionist methods employ deep neural networks to automatically learn visual and linguistic features and map to a sequence of low-level actions, in an endto-end fashion. These two approaches are blended to create a hybrid, modular framework: it formulates instruction following as symbolic goal learning via deep neural networks followed by task planning via symbolic planners. Connectionist and symbolic modules are bridged with Planning Domain Definition Language. The vision-and-language learning network predicts its goal representation, which is sent to a planner for producing a task-completing action sequence. For improving the ... : 8 pages, 3 figures, 3 tables ...
|
|
Keyword:
FOS Computer and information sciences; Robotics cs.RO
|
|
URL: https://arxiv.org/abs/2202.12912 https://dx.doi.org/10.48550/arxiv.2202.12912
|
|
BASE
|
|
Hide details
|
|
12 |
Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Interactive Robotic Grasping with Attribute-Guided Disambiguation ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
The Enforcers: Consistent Sparse-Discrete Methods for Constraining Informative Emergent Communication ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
The Construction of the Robot in Language and Culture, “Intercultural Robotics” and the “Third Robot Culture” ...
|
|
Cheng, Lin. - : Technology and Language, 3(1), 1-8, 2022
|
|
BASE
|
|
Show details
|
|
17 |
The influence of animacy on perspective-taking and word order during language production ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Machine infelicity in a poignant visitor setting: Comparing human and AI’s ability to analyze discourse
|
|
|
|
In: Research outputs 2014 to 2021 (2022)
|
|
BASE
|
|
Show details
|
|
20 |
Integrating Blockchains and Intelligent Agents in the Pursuit of Artificial General Intelligence
|
|
|
|
In: Senior Honors Theses (2022)
|
|
BASE
|
|
Show details
|
|
|
|