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23 papers · ranked by Valyu relevance

01
AarXiv23 Jan 2009
Interactive Policy Learning through Confidence-Based Autonomy
Sonia Chernova, Manuela Veloso
We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration. The CBA algorithm consists of two components which take advantage of the complimentary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in…
94relevance
02
PPubMed18 Jul 2024
Semantic learning from keyframe demonstration using object attribute constraints Sen et al. 10.3389/frobt.2024.1340334
Busra Sen, Jos Elfring, Elena Torta, René van de Molengraft
Learning from demonstration is an approach that allows users to personalize a robot’s tasks. While demonstrations often focus on conveying the robot’s motion or task plans, they can also communicate user intentions through object attributes in manipulation tasks. For instance, users might want to teach a robot to sort…
89relevance
03
PPubMed21 Apr 2020
Generalize Robot Learning From Demonstration to Variant Scenarios With Evolutionary Policy Gradient
Junjie Cao, Weiwei Liu, Yong Liu, Jian Yang
There has been substantial growth in research on the robot automation, which aims to make robots capable of directly interacting with the world or human. Robot learning for automation from human demonstration is central to such situation. However, the dependence of demonstration restricts robot to a fixed scenario…
88relevance
04
AarXiv2023
A Survey of Demonstration Learning
André Correia, Luı́s A. Alexandre
With the fast improvement of machine learning, reinforcement learning (RL) has been used to automate human tasks in different areas. However, training such agents is difficult and restricted to expert users. Moreover, it is mostly limited to simulation environments due to the high cost and safety concerns of…
87relevance
05
PPubMed2 Aug 2021
O 2 A: One-Shot Observational Learning with Action Vectors Pauly et al. One-Shot Observational Learning
Leo Pauly, Wisdom C. Agboh, David C. Hogg, Raul Fuentes
We present O2A, a novel method for learning to perform robotic manipulation tasks from a single (one-shot) third-person demonstration video. To our knowledge, it is the first time this has been done for a single demonstration. The key novelty lies in pre-training a feature extractor for creating a perceptual…
86relevance
06
PPubMed31 Mar 2022
Vision-Based Learning from Demonstration System for Robot Arms
Pin-Jui Hwang, Chen-Chien Hsu, Po-Yung Chou, Wei-Yen Wang + 2 more
'Cheng-Hung Lin' 'Oscar Reinoso Garcia'] Robotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and optimized when new tasks are…
86relevance
07
AarXiv2019
Interactive Learning of Environment Dynamics for Sequential Tasks
Robert Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman + 1 more
'David L. Roberts'] In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but also the structure and dynamics of that user's environment. While existing work has looked at how the goals of a task can…
85relevance
08
PPubMed16 Sep 2021
Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration
Ning Zhang, Tao Qi, Yongjia Zhao, Denis Laurendeau
Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system that uses virtual reality equipment for assembly activities…
85relevance
09
PPubMed1 Jun 2021
Reinforcement Learning With Human Advice: A Survey
Anis Najar, Mohamed Chetouani
In this paper, we provide an overview of the existing methods for integrating human advice into a reinforcement learning process. We first propose a taxonomy of the different forms of advice that can be provided to a learning agent. We then describe the methods that can be used for interpreting advice when its meaning…
83relevance
10
PPubMed30 Jun 2021
Machine Teaching for Human Inverse Reinforcement Learning Lee et al. Machine Teaching for Human IRL
Michael S. Lee, Henny Admoni, Reid Simmons
As robots continue to acquire useful skills, their ability to teach their expertise will provide humans the two-fold benefit of learning from robots and collaborating fluently with them. For example, robot tutors could teach handwriting to individual students and delivery robots could convey their navigation…
83relevance
11
AarXiv2020
Behavior Cloning in OpenAI using Case Based Reasoning
C. J. Peters, Babak Esfandiari, Mohamad Zalat, Robert West
Learning from Observation (LfO), also known as Behavioral Cloning, is an approach for building software agents by recording the behavior of an expert (human or artificial) and using the recorded data to generate the required behavior. jLOAF is a platform that uses Case-Based Reasoning to achieve LfO. In this paper we…
82relevance
12
BbioRxiv8 Oct 2019
Imitation as a model-free process in human reinforcement learning
Anis Najar, Emmanuelle Bonnet, Bahador Bahrami, Stefano Palminteri
While there is not doubt that social signals affect human reinforcement learning, there is still no consensus about their exact computational implementation. To address this issue, we compared three hypotheses about the algorithmic implementation of imitation in human reinforcement learning. A first hypothesis…
82relevance
13
AarXiv2 Aug 2020
Interactive Imitation Learning in State-Space
Snehal Jauhri, Carlos Celemin, Jens Kober
Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing…
80relevance
14
BbioRxiv13 Feb 2025
Cortical control of innate behavior from subcortical demonstration
Jason A. Keller, Iljung S. Kwak, Alyssa K. Stark, Marius Pachitariu + 2 more
Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top ^1–3^. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning ^4^. Although a feasible…
80relevance
15
BbioRxiv5 Dec 2023
Adaptive algorithms for shaping behavior
William L. Tong, Anisha Iyer, Venkatesh N. Murthy, Gautam Reddy
Dogs and laboratory mice are commonly trained to perform complex tasks by guiding them through a curriculum of simpler tasks (‘shaping’). What are the principles behind effective shaping strategies? Here, we propose a machine learning framework for shaping animal behavior, where an autonomous teacher agent decides its…
67relevance
16
CchemRxiv4 Jul 2022
Human-in-the-loop assisted de novo molecular design
Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos + 5 more
A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still…
65relevance
17
BbioRxiv29 May 2019
The dynamics of motor learning through the formation of internal models
Camilla Pierella, Maura Casadio, Sara A. Solla, Ferinando A. Mussa-Ivaldi
A medical student learning to perform a laparoscopic procedure as well as a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for the external device. Mathematically, we describe this type of learning as a deterministic dynamical…
63relevance
18
CchemRxiv10 Jul 2025
LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
63relevance
19
BbioRxiv4 May 2025
Task-Relevant Haptic Feedback Enhances de novo Acquisition of Novel Arm Control
Ian S. Howard, Laura Alvarez-Hidalgo
The human motor system can learn to control novel effectors, but the contribution of task-relevant haptic dynamics to de novo learning remains unclear. Using a bimanual robotic interface, participants learned over two days to control the shoulder and elbow angles of a virtual arm in order to achieve accurate endpoint…
62relevance
20
BbioRxiv13 Jul 2022
“Leap before you look”: Conditions that promote implicit visuomotor adaptation without explicit learning
Tejas Savalia, Rosemary A. Cowell, David E. Huber
When learning a novel visuomotor mapping (e.g., mirror writing), accuracy can improve quickly through explicit learning (e.g., move left to go right) but after considerable practice, implicit learning takes over, producing fast, natural movements. This implicit learning occurs automatically, but it has been unknown…
62relevance
21
CchemRxiv10 Jul 2023
CRESt – Copilot for Real-world Experimental Scientist
Zhichu Ren, Zhen Zhang, Yunsheng Tian, Ju Li
Autonomous laboratories were previously controlled mainly by scripting languages such as Python, limiting their usage among experimentalists. The recent release of OpenAI's ChatGPT API's function calling feature has enabled seamless integration and execution of Python subroutines in experimental workflows using voice…
61relevance
22
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
57relevance
23
CchemRxiv8 Nov 2023
Traversing Chemical Space with Active Deep Learning
Derek van Tilborg, Francesca Grisoni
Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active deep learning has an untapped potential for low-data drug discovery, as it allows to improve a model iteratively during the screening…
51relevance

Results for Learning from Demonstration

Interactive Policy Learning through Confidence-Based Autonomy

Semantic learning from keyframe demonstration using object attribute constraints Sen et al. 10.3389/frobt.2024.1340334

Generalize Robot Learning From Demonstration to Variant Scenarios With Evolutionary Policy Gradient

A Survey of Demonstration Learning

O 2 A: One-Shot Observational Learning with Action Vectors Pauly et al. One-Shot Observational Learning

Vision-Based Learning from Demonstration System for Robot Arms

Interactive Learning of Environment Dynamics for Sequential Tasks

Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration

Reinforcement Learning With Human Advice: A Survey

Machine Teaching for Human Inverse Reinforcement Learning Lee et al. Machine Teaching for Human IRL

Behavior Cloning in OpenAI using Case Based Reasoning

Imitation as a model-free process in human reinforcement learning

Interactive Imitation Learning in State-Space

Cortical control of innate behavior from subcortical demonstration

Adaptive algorithms for shaping behavior

Human-in-the-loop assisted de novo molecular design

The dynamics of motor learning through the formation of internal models

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

Task-Relevant Haptic Feedback Enhances de novo Acquisition of Novel Arm Control

“Leap before you look”: Conditions that promote implicit visuomotor adaptation without explicit learning

CRESt – Copilot for Real-world Experimental Scientist

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Traversing Chemical Space with Active Deep Learning

Interactive Policy Learning through Confidence-Based Autonomy

Semantic learning from keyframe demonstration using object attribute constraints Sen et al. 10.3389/frobt.2024.1340334

Generalize Robot Learning From Demonstration to Variant Scenarios With Evolutionary Policy Gradient

A Survey of Demonstration Learning

O 2 A: One-Shot Observational Learning with Action Vectors Pauly et al. One-Shot Observational Learning

Vision-Based Learning from Demonstration System for Robot Arms

Interactive Learning of Environment Dynamics for Sequential Tasks

Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration

Reinforcement Learning With Human Advice: A Survey

Machine Teaching for Human Inverse Reinforcement Learning Lee et al. Machine Teaching for Human IRL

Behavior Cloning in OpenAI using Case Based Reasoning

Imitation as a model-free process in human reinforcement learning

Interactive Imitation Learning in State-Space

Cortical control of innate behavior from subcortical demonstration

Adaptive algorithms for shaping behavior

Human-in-the-loop assisted de novo molecular design

The dynamics of motor learning through the formation of internal models

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

Task-Relevant Haptic Feedback Enhances de novo Acquisition of Novel Arm Control

“Leap before you look”: Conditions that promote implicit visuomotor adaptation without explicit learning

CRESt – Copilot for Real-world Experimental Scientist

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Traversing Chemical Space with Active Deep Learning