Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani,Ajay Kumar Tanwani,Jonathan Lee,Brijen Thananjeyan,Michael Laskey,Sanjay Krishnan,Roy Fox,Ken Goldberg,Sylvain Calinon +8 more
- 09 Dec 2018
- pp 196-211
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Citations
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
Ajay Kumar Tanwani,Pierre Sermanet,Andy Yan,Raghav Anand,Mariano Phielipp,Ken Goldberg +5 more
- 31 May 2020
TL;DR: This paper learns a motion-centric representation of surgical video demonstrations by grouping them into action segments/subgoals/options in a semi-supervised manner and demonstrates the use of this representation to imitate surgical suturing kinematic motions from publicly available videos of the JIGSAWS dataset.
45
Learning robust manipulation tasks involving contact using trajectory parameterized probabilistic principal component analysis
Cristian Alejandro Vergara Perico,Joris De Schutter,Erwin Aertbeliën +2 more
- 24 Oct 2020
TL;DR: In this article, Trajectory parameterized Probabilistic Principal Component Analysis (traPPCA) is introduced to learn manipulation tasks involving both motion and contact wrenches (forces and moments).
10
Sequential robot imitation learning from observations
TL;DR: In this paper, a framework to learn the sequential structure in the demonstrations for robot imitation learning is presented. But this framework is not suitable for the task of human imitation learning, as shown in Figure 1.
9
•Proceedings Article
Mitigating Network Latency in Cloud-Based Teleoperation using Motion Segmentation and Synthesis
Nan Tian,Ajay Kumar Tanwani,Ken Goldberg,Somayeh Sojoudi +3 more
- 01 Oct 2019
Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints
Akshay Dhonthi,Philipp Schillinger,Leonel Rozo,Daniele Nardi +3 more
- 07 Sep 2022
TL;DR: Signal Temporal Logic (STL), an expressive form of temporal properties of systems, is used to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly.
6
References
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Lawrence R. Rabiner
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A survey of robot learning from demonstration
TL;DR: A comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings, which analyzes and categorizes the multiple ways in which examples are gathered, as well as the various techniques for policy derivation.
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Hierarchical Dirichlet Processes
TL;DR: This work considers problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups, and considers a hierarchical model, specifically one in which the base measure for the childDirichlet processes is itself distributed according to a Dirichlet process.
Mixtures of probabilistic principal component analyzers
TL;DR: PCA is formulated within a maximum likelihood framework, based on a specific form of gaussian latent variable model, which leads to a well-defined mixture model for probabilistic principal component analyzers, whose parameters can be determined using an expectation-maximization algorithm.
•Posted Content
Generative Adversarial Imitation Learning
Jonathan Ho,Stefano Ermon +1 more
TL;DR: A new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning, is proposed and a certain instantiation of this framework draws an analogy between imitation learning and generative adversarial networks.
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