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Can We Learn Finite State Machine Robot Controllers from Interactive Demonstration?
[chapter]
2010
Studies in Computational Intelligence
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There is currently a gap between the types of robot control polices that can be learnt from interactive demonstration and those manually programmed by skilled coders. ...
We examine the limits of a regression-based approach for learning an FSM controller from demonstration of a basic robot soccer goal-scoring task. ...
Introduction We investigate regression-based interactive Robot Learning from Demonstration (RLfD) for use in instantiating a soccer-style goal-scoring Finite State Machine (FSM) controller on a robot dog ...
doi:10.1007/978-3-642-05181-4_17
fatcat:zoohodjrjfasjltavzzwrmf2nm
Automata Guided Reinforcement Learning With Demonstrations
[article]
2018
arXiv
pre-print
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We propose to address these problems by combining temporal logic (TL) with reinforcement learning from demonstrations. ...
The policy resulting from our framework has an interpretable and hierarchical structure. We validate the proposed method experimentally on a set of robotic manipulation tasks. ...
φ , D Q ) RLf D stands for any learning from demonstration algorithm Finite state automaton generated from formula ♦(r ∧ ♦(g ∧ ♦b)). ...
arXiv:1809.06305v2
fatcat:oe4j2zt53bbuthzfivgi6k4mzq
Incremental Semantically Grounded Learning from Demonstration
2013
Robotics: Science and Systems IX
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Much recent work in robot learning from demonstration has focused on automatically segmenting continuous task demonstrations into simpler, reusable primitives. ...
We introduce a novel method for discovering semantically grounded primitives and incrementally building and improving a finite-state representation of a task in which various contingencies can arise. ...
For this reason, robot learning from demonstration (LfD) [2] has become a popular way to program robots. ...
doi:10.15607/rss.2013.ix.048
dblp:conf/rss/NiekumCBMO13
fatcat:msseulkzbbhtngzkgmsbkxn3li
Recent Advances in Robot Learning from Demonstration
2019
Annual Review of Control Robotics and Autonomous Systems
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In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. ...
This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. ...
(152) used unstructured demonstrations and interactive corrections to learn finite state machines that are made of several trajectory models. Pastor et al. ...
doi:10.1146/annurev-control-100819-063206
fatcat:gz56an5s6zh7da7my4ix7gdj4q
Incremental learning of subtasks from unsegmented demonstration
2010
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems
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We illustrate the applicability of our technique by learning a suitable set of subtasks from the demonstration of a finite-state machine robot soccer goal scorer. ...
Previous robot learning from demonstration techniques have either learned the individual subtasks in isolation, combined known subtasks, or used knowledge of the overall task structure to perform segmentation ...
Such policies are known to occur in Finite-State-Machine (FSM) controllers [7] . ...
doi:10.1109/iros.2010.5650500
dblp:conf/iros/GrollmanJ10
fatcat:hmbggdnzszcwtg2w6ikblcoeyq
Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations
[article]
2018
arXiv
pre-print
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Learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures. ...
We present additional conditions, which linearly bound the difference in state at each time step by the magnitude of control, allowing us to prove that the robot will not violate the constraints using ...
With learning from demonstrations, a robot observes a supervisor policy and learns a mapping from state to control via regression. ...
arXiv:1801.10321v3
fatcat:npghadqgnzbpjlkzb76q3hd5k4
Learning Autonomous Mobility Using Real Demonstration Data
[article]
2021
arXiv
pre-print
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We formulated an architecture based on a long-short-term-memory (LSTM) neural network, which effectively learn reactive control policies from human demonstrations. ...
Furthermore, we proposed a method to efficiently handle non-optimal demos and to learn new skills, since collecting enough demonstration can be time-consuming and sometimes very difficult on a real robotic ...
So we train the mobility controller and the manipulation controller separately. A finite state machine is use to control the working models of the robot (Fig.3) .
A. ...
arXiv:2108.04792v1
fatcat:kbwdz5yspjgvjpo4oa7jj4xk3a
Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition
[chapter]
2015
Lecture Notes in Computer Science
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Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. ...
Finally, we propose a new RoboCup Technical Challenge in multiagent learning from demonstration. ...
Training an automaton only involves learning its transition functions. In "training mode" the HFA transitions from state to state only when told to by the demonstrator. ...
doi:10.1007/978-3-319-18615-3_30
fatcat:23mgwl2iqfhwzccmpxacrns7wu
Learning grounded finite-state representations from unstructured demonstrations
2014
The international journal of robotics research
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For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. ...
By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. ...
Then, building on these learned skills, we incrementally learn and improve a finite-state representation of a task that allows for intelligent, adaptive sequencing of skills and recovery from errors. ...
doi:10.1177/0278364914554471
fatcat:vjgxlppa45g2xp5opisrn4n7su
Learning Sensory-Motor Associations from Demonstration
[article]
2020
arXiv
pre-print
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We propose a method which generates reactive robot behavior learned from human demonstration. ...
As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space. ...
Next we will investigate how this approach can be used to learn evaluations end-to-end from the raw kinematic states directly and how it could work alongside another learning system which allows for higher ...
arXiv:1903.01352v4
fatcat:htcb3daohrhmzihlul4ierybsa
ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
[article]
2021
arXiv
pre-print
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Besides supporting the learning of policies from interactions, we also support learning-from-demonstrations (LfD) methods, by providing a large number of high-quality demonstrations (~36,000 successful ...
We provide baselines using 3D deep learning and LfD algorithms. ...
building robots, Jiayuan Gu for providing technical support on SAPIEN, and Rui Chen, Songfang Han, Wei Jiang for testing our system. ...
arXiv:2107.14483v5
fatcat:5rgpkfezajdidkxk56r5jpj7ri
Incremental Learning Introspective Movement Primitives from Multimodal Unstructured Demonstrations
2019
IEEE Access
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Learning movement primitive from unstructured demonstrations has become a popular topic in recent years, which provides a natural way to endow human-inspired skills to robots. ...
INDEX TERMS Introspection, movement primitives, unstructured demonstration, Bayesian nonparametric learning, reverse execution, human interaction, kitting experiment. ...
Consequently, we hopefully learn the robot complex task representation by formulating each state of constructed finite state machine using a state-specific movement primitive technique.
3) LEARNING MOVEMENT ...
doi:10.1109/access.2019.2947529
fatcat:hwypvbiz55clrkfwcg5ojkjsyq
Interactive robot task training through dialog and demonstration
2007
Proceeding of the ACM/IEEE international conference on Human-robot interaction - HRI '07
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We present a framework for interactive task training of a mobile robot where the robot learns how to do various tasks while observing a human. ...
In this paper, we describe the task training framework, describe how environmental context and communicative dialog with the human help the robot learn the task, and illustrate the utility of this approach ...
This research was supported by the National Business Center (NBC) of the Department of the Interior (DOI) under a subcontract from SRI International. ...
doi:10.1145/1228716.1228724
dblp:conf/hri/RybskiYSV07
fatcat:4nzmohni3zdrrjoyapx4qkvw7m
Learning Constraints from Demonstrations
[article]
2019
arXiv
pre-print
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We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations. ...
We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of ...
Our work is also relevant to human-robot interaction. ...
arXiv:1812.07084v2
fatcat:kq4y4jexbzfrrnv7yqit7yuejm
Learning Cooperative Dynamic Manipulation Skills from Human Demonstration Videos
[article]
2022
arXiv
pre-print
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This article proposes a method for learning and robotic replication of dynamic collaborative tasks from offline videos. ...
The objective is to extend the concept of learning from demonstration (LfD) to dynamic scenarios, benefiting from widely available or easily producible offline videos. ...
planner and a Finite State Machine (FSM). ...
arXiv:2204.04003v1
fatcat:2fdlyus6zfgvtftuf3kouoowfy
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