Learning from Demonstration

By Stefan Schaal. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

Citations in the corpus

From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015, methodology): “erlying dynamical system, which are then used for internal simulations and policy learning. These ideas have been successfully applied to RL, control and robotics in (Schmidhuber, 1990; Atkeson & Schaal, 1997; Bagnell & Schneider, 2001; Contardo et al., 2013; arXiv:1502.02251v3 [stat.ML] 18 ”
Observe and Look Further: Achieving Consistent Performance on Atari (2018, background): “Some early works in this area [1, 17] used expert demonstrations to ﬁnd a good initial policy before ﬁne-tuning it with RL.”
Accelerating Online Reinforcement Learning with Offline Datasets (2020, background): “This data can potentially come from demonstrations for the desired task [16, 2], suboptimal policies [6], demonstrations for related tasks [21], or even just random exploration in the environment.”
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation (2020, neutral): “However, current model-based RL algorithms may converge to suboptimal solutions [32].”
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation (2020, background): “Leveraging human guidance for RL tasks has been extensively studied in the context of imitation learning (learning from demonstration) [37], inverse reinforcement learning [29, 1], reward shaping [28], learning from human preference [7, 22], and learning from saliency information provided by human…”
Reinforcement learning in robotics: A survey (2013, methodology): “However, good features are well known in robotics only for a few problems, such as features for local stabilization (Schaal, 1996) and features describing rigid body dynamics (An et al.”
Deep Q-learning From Demonstrations (2017, background): “Another work that is similarly motivated to ours is (Schaal 1996).”
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”
Acme: A Research Framework for Distributed Reinforcement Learning (2020, background): “Learning from Demonstrations In the Learning from Demonstrations (LfD) setting (Schaal, 1997; Hester et al., 2018; Vecerik et al., 2017a; Dadashi et al., 2022), similarly to the standard online RL setting, the goal is to learn a policy through trial and error by interacting with an environment, whic”
An Algorithmic Perspective on Imitation Learning (2018, background): “The term “learning from demonstration” has become very popular since its use by S. Schaal and G. Atkeson [Schaal, 1997, Atkeson and Schaal, 1997].”
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning (2011, background): “This assumption leads to “model bias”, which often makes learning from scratch “daunting” [22], especially when only a few samples from the system are available.”
Behavioral Cloning from Observation (2018, background): “Enabling this same ability in autonomous agents, referred to as learning from demonstration (LfD) , has been given a great deal of attention in the research community [Schaal, 1997; Argall et al. , 2009].”
Gaussian Processes for Data-Efficient Learning in Robotics and Control (2015, neutral): “In this article, we assume that expert knowledge (e.g., in terms of expert demonstrations [48], realistic simulators, or explicit differential equations for the dynamics) is unavaiable. v 1502 Instead, we carefully model the observed dynamics using a general ﬂexible nonparametric approach.”
A Divergence Minimization Perspective on Imitation Learning Methods (2019, methodology): “In many cases however, it is either too challenging to optimize a given reward (e.g. due to sparsity of signal), or it is simply impossible to design a reward function that captures the intricate details of desired outcomes [3, 4, 5, 6, 7, 8].”
Task-Relevant Adversarial Imitation Learning (2019, methodology): “, 1996] with approaches ranging from Q-learning [Schaal, 1997] to behavioral cloning (BC) [Pomerleau, 1989].”
Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation (2018, background): “s, their reward function aims to minimise the distance in position and rotation between the expert demonstrations and the generated sequences. Imitation learning (IL), or learning from demonstrations [30] is the process through which, given a set of demonstrations, an agent learns to map observation”
A Bayesian Approach for Policy Learning from Trajectory Preference Queries (2012, background): “via imitation learning [15, 3, 13] or inverse reinforcement learning [12].”
Efficiently Guiding Imitation Learning Algorithms with Human Gaze (2020, methodology): “Imitation learning (IL) (Schaal 1997),(Argall et al. 2009) is an alternative methodology to infer an optimal policy from demonstrations.”