A survey of robot learning from demonstration

By Brenna D. Argall, Sonia Chernova, Manuela M. Veloso, Brett Browning. 2009

4,034 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Reinforcement Learning

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Citations in the corpus

One-Shot Imitation Learning (2017, background): “Survey articles include (Schaal, 1999; Calinon, 2009; Argall et al., 2009).”
Scalable agent alignment via reward modeling: a research direction (2018, background): “…been explored in the literature: providing a set of demonstrations of the desired behavior (Russell, 1998; Ng & Russell, 2000; Abbeel & Ng, 2004; Argall et al., 2009); providing feedback in the form of scores (El Asri et al., 2016), actions (Grifﬁth et al., 2013), value (Knox & Stone, 2009),…”
Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “ preferences over trajectories through interactive feedback from the user in a co-active learning setting [30]. Unlike in other learning settings, where a human ﬁrst demonstrates optimal trajectories [3] for a task to the robot, our learning model does not rely on the users ability to demonstrate op”
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning (2010, methodology): “…are used to learn a controller, have proven very useful in practice and have led to state-of-the art performance in a variety of applications (Schaal, 1999; Abbeel and Ng, 2004; Ratliff et al., 2006; Silver et al., 2008; Argall et al., 2009; Chernova and Veloso, 2009; Ross and Bagnell, 2010).”
Time-Contrastive Networks: Self-Supervised Learning from Video (2017, background): “Imitation learning: Imitation learning [3] has been widely”
Plan explicability and predictability for robot task planning (2015, background): “In the human-robot interaction (HRI) community, there exists prior works that discuss how to enable natural and fluent human-robot interaction [11, 12, 21, 18] to create more socially acceptable robots [7].”
Off-Policy Deep Reinforcement Learning without Exploration (2018, background): “Imitation learning and its variants are well studied problems (Schaal, 1999; Argall et al., 2009; Hussein et al., 2017).”
Reinforcement learning in robotics: A survey (2013, background): “For a recent survey detailing the state of the art in imitation learning for robotics, see Argall et al. (2009).”
End-to-End Driving Via Conditional Imitation Learning (2017, background): “Imitation learning has been applied to a variety of tasks, including articulated motion [2], [28], [11], autonomous flight [1], [13], [30], modeling navigational behavior [37], [38], off-road driving [22], [32], and road following [4], [6], [27], [36].”
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling (2019, methodology): “Imitation learning is commonly adopted as a standard approach in robotics (Pomerleau, 1989; Schaal, 1997; Argall et al., 2009; Osa et al., 2017; Ye & Alterovitz, 2017; Aleotti & Caselli, 2006; Lawitzky et al., 2012; Torabi et al., 2018; Le et al., 2017; 2018) and many other areas such as playing gam”
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations (2017, background): “[5] provide a comprehensive survey of imitation learning methods in robotics - including other methods such as inverse reinforcement learning.”
Hyperparameter Selection for Imitation Learning (2021, background): “One of the approaches to overcome this issue is Imitation Learning (IL), which relies on a set of demonstrations presenting the desired behaviour instead of a reward signal (Schaal, 1999; Argall et al., 2009).”
Visual Imitation Made Easy (2020, background): “For a more comprehensive review of imitation learning, we point the readers to Argall et al. [16].”
Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance (2019, background): “Three participants with High experience had some prior experience with LfD and kinesthetic teaching.”
Self-Supervised Correspondence in Visuomotor Policy Learning (2019, background): “We point the reader to a number of existing reviews of learning from demonstration [27], [28].”
Leveraging Human Guidance for Deep Reinforcement Learning Tasks (2019, background): “Most imitation learning or learning from demonstration algorithms assume this type of guidance [ Schaal, 1999; Argall et al. , 2009; Osa et al. , 2018].”
Simitate: A Hybrid Imitation Learning Benchmark (2019, background): “The importance of such a metric has already been highlighted in 2009 [16] and again in 2018 [17].”
ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018, background): “One popular option is to kinesthetically guide a robot through a desired trajectory [14].”
Learning from Physical Human Corrections, One Feature at a Time (2018, background): “Realistically, however, human corrections are noisy — even for expert users [2] — and will not necessarily induce the optimal feature difference during every correction.”
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2017, methodology): “Imitation learning is a class of methods for acquiring skills by observing demonstrations (see, e.g., [1], [2], [3] for surveys).”