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What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
[article]
2021
arXiv
pre-print
Our study analyzes the most critical challenges when learning from offline human data for manipulation. ...
In this paper, we conduct an extensive study of six offline learning algorithms for robot manipulation on five simulated and three real-world multi-stage manipulation tasks of varying complexity, and with ...
physical robot tasks. ...
arXiv:2108.03298v2
fatcat:p7fmn5qjynftbjaymffoifpf5y
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
[article]
2020
arXiv
pre-print
Learning from offline task demonstrations is a problem of great interest in robotics. ...
For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task. ...
We thank Kevin Shih for help with training models on image observations. We thank members of the NVIDIA Seattle Robotics Lab for several helpful discussions and feedback. ...
arXiv:1911.05321v2
fatcat:q6kp2tjdrreu3oefw5dyczulj4
Robot learning of industrial assembly task via human demonstrations
2018
Autonomous Robots
The traditional robot programming cannot cope with these challenges of human-robot collaboration. In this paper, a framework for robot learning by multiple human demonstrations is introduced. ...
Additionally, the robot learns every path as needed for object manipulation (low-level learning). ...
The authors thank MeRoSy industrial project partner pi4 robotics GmbH for the technical support in assembly scenario. ...
doi:10.1007/s10514-018-9725-6
fatcat:pgxk6qhfffguvpjxxbrkbvfdjq
Benchmark for Bimanual Robotic Manipulation of Semi-deformable Objects
2020
IEEE Robotics and Automation Letters
We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. ...
Index Terms-Performance evaluation and benchmarking, dual arm manipulation, model learning for control, dexterous manipulation. ...
Zhu et al. survey in [27] the approaches to learn the assembly task using Learning from Demonstration, from either kinesthetic, motion-sensor or teleoperated demonstrations. ...
doi:10.1109/lra.2020.2972837
fatcat:bdchalm6wfhm5euyizpc5w4c3q
Batch Exploration with Examples for Scalable Robotic Reinforcement Learning
[article]
2020
arXiv
pre-print
Learning from diverse offline datasets is a promising path towards learning general purpose robotic agents. ...
However, a core challenge in this paradigm lies in collecting large amounts of meaningful data, while not depending on a human in the loop for data collection. ...
By minimizing the need for a human in the loop, this framework enables scalably collecting and learning from robotic data. provides some weak supervision to the agent to indicate what regions of the state ...
arXiv:2010.11917v1
fatcat:xu2kw5ffhvdulbs3rrsek2bf3y
Research Trends in Social Robots for Learning
2020
Current Robotics Reports
A trend analysis is then proposed demonstrating the potential for educational context to nest impactful research from human-robot interaction. ...
Secondly, we explore the potential for education to be the ideal context to investigate central human-robot interaction research questions. ...
What can be taught with a social robot? What is the role of social robots in education? & Social adaptation and personalized learning: Can educational paradigm be applied to robots for learning? ...
doi:10.1007/s43154-020-00008-3
fatcat:ydoinppcwrh27ejewwyas42vhq
The Surprising Effectiveness of Representation Learning for Visual Imitation
[article]
2021
arXiv
pre-print
Such joint learning causes an interdependence between these two problems, which often results in needing large amounts of demonstrations for learning. ...
We experimentally show that this simple decoupling improves the performance of visual imitation models on both offline demonstration datasets and real-robot door opening compared to prior work in visual ...
lightweight mobile manipulator for indoor human envi-
Improved baselines with momentum contrastive learning. ronments. arXiv preprint arXiv:2109.10892, 2021. ...
arXiv:2112.01511v2
fatcat:qsysmd56mbdipkx2jt5wbkzrru
Quantifying Hypothesis Space Misspecification in Learning From Human–Robot Demonstrations and Physical Corrections
2020
IEEE Transactions on robotics
We demonstrate our method on a 7 degree-of-freedom robot manipulator in learning from two important types of human input: demonstrations of manipulation tasks, and physical corrections during the robot's ...
Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives. ...
However, longer offline computation is possible in our learning-from-demonstrations scenario as the inference happens offline, after providing the robot with human demonstrations. ...
doi:10.1109/tro.2020.2971415
fatcat:btlfyed7ibdqddogkmtwcigxuu
Vision for Autonomous Vehicles and Probes (Dagstuhl Seminar 15461)
2016
Dagstuhl Reports
Computer vision plays a key role in advanced driver assistance systems (ADAS) as well as in exploratory and service robotics. ...
In 2013, Daimler succeeded autonomous driving on a public drive way. Today, the Curiosity mars rover is sending video views from Mars to Earth. ...
Who Where
and what is included in the environment maps? we should know limitations of online/offline mapping! what is the difference between online/offline mapping? ...
doi:10.4230/dagrep.5.11.36
dblp:journals/dagstuhl-reports/BruhnILP15
fatcat:l2nqd45tnrabpdqmwex6enkxei
Learning from Humans
[chapter]
2016
Springer Handbook of Robotics
The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imitation learning. ...
This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. ...
Reinforcement learning (RL) appeared particularly suitable for type of problem. Indeed, imitation learning is limiting in that it requires the robot to learn only from what has been demonstrated. ...
doi:10.1007/978-3-319-32552-1_74
fatcat:wtcftkgkwveexpfbmnkcebi5wu
PLATO: Predicting Latent Affordances Through Object-Centric Play
[article]
2022
arXiv
pre-print
Due to this diverse coverage, existing approaches for learning from play are more robust to online policy deviations from the offline data distribution. ...
Constructing a diverse repertoire of manipulation skills in a scalable fashion remains an unsolved challenge in robotics. ...
Imitation Learning is a common method for learning robot policies from human demonstrations, where a policy is trained to mimic the demonstrated actions [22] . ...
arXiv:2203.05630v1
fatcat:zmhd4h5qlzfylhd7zkr6sezkiu
What Matters in Language Conditioned Robotic Imitation Learning
[article]
2022
arXiv
pre-print
In this paper, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. ...
While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design ...
, it is difficult to asses what matters for language conditioned policy learning. ...
arXiv:2204.06252v1
fatcat:xnq35sp7xvelpfubinjpraec7a
Offline Reinforcement Learning as Anti-Exploration
[article]
2021
arXiv
pre-print
Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed dataset, without interactions with the system. ...
We thus take inspiration from the literature on bonus-based exploration to design a new offline RL agent. ...
Learning
complex dexterous manipulation with deep reinforcement learning and demonstrations. Proceedings of
Robotics: Science and Systems (RSS), 2017.
[43] D. J. Rezende, S. Mohamed, and D. ...
arXiv:2106.06431v1
fatcat:crppp6covnc7plgf6uttkwyili
Affordances in Robotic Tasks – A Survey
[article]
2020
arXiv
pre-print
Affordances are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects. ...
specifications useful for robots. ...
[54] improve the classification rate by learning from human demonstration to grasp objects in a household task. ...
arXiv:2004.07400v1
fatcat:hiexg6suzvg6ld7cwjtzyz4qka
Planning, Learning and Reasoning Framework for Robot Truck Unloading
[article]
2020
arXiv
pre-print
We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. ...
In particular, motion planning and execution modules are evaluated in simulation and on a real robot, while offline learning and online decision-making are evaluated in simulated real-world scenarios. ...
In addition to the real robot, we also employ a simulation of the robot, trailer, and boxes to estimate the outcomes of actions, and perform offline learning. ...
arXiv:1910.09453v2
fatcat:xrry6dfwdbgw5pu6rpvej6hd3m
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