Learning to select and generalize striking movements in robot table tennis

By Katharina Muelling, Jens Kober, Oliver Kroemer, Jan Peters. 2012

478 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Control and Systems Engineering, Robotics & Control

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Citations in the corpus

Time-Contrastive Networks: Self-Supervised Learning from Video (2017, methodology): “used for learning robotic skills from expert demonstrations [4, 5, 6, 7] and can be split into two areas: be-”
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning (2019, background): “While reinforcement learning (RL) has achieved some success in domains such as assembly [1], ping pong [2], in-hand manipulation [3], and hockey [4], state-of-the-art methods require substantially more experience than humans to acquire only one narrowly-defined skill.”
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation (2018, background): “n expert demonstration vastly cuts down the exploration space and can provide strong guidance during policy learning [34]. This has enabled autonomous helicopter aerobatics [35], table-tennis playing [36] and drone ﬂying [37]. However these methods often focus on learning from a handful of expert de”
One-Shot Visual Imitation Learning via Meta-Learning (2017, methodology): “n of that task by using a dataset of demonstrations of many other tasks for meta-learning. Sharing information across tasks is by no means a new idea, e.g. by using task-to-task mappings [22], gating [23], and shared features [24]. These multi-task robotic learning methods consider the problem of ge”
An Algorithmic Perspective on Imitation Learning (2018, methodology): “Dynamic movement primitives (DMPs) [Schaal et al., 2004, Ijspeert et al., 2013] and the stable estimator of dynamical systems (SEDS) approach [Khansari-Zadeh and Billard, 2011] are representations that explicitly satisfy the condition of smooth convergence to the goal state.”
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning (2018, background): “acquire a range of complex skills through demonstration, such as table tennis [28], lane following [34], pouring water [31], drawer opening [38], and multi-stage manipulation tasks [62].”
Learning dexterous manipulation for a soft robotic hand from human demonstrations (2016, methodology): “This has been used to learn striking movements [13] and bi-manual transportation tasks [14].”
Language-Conditioned Imitation Learning for Robot Manipulation Tasks (2020, background): “Learning robot control policies by imitation [31] is an appealing approach to skill acquisition and has been successfully applied to several tasks, including locomotion, grasping, and even table tennis [8, 2, 25].”
Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018, background): “trollers deal with a single task. In a robotics context, solutions for multiple related tasks are often desired, e.g., for grasping multiple objects [22] or in robot games, such as robot table tennis [24] or soccer [3]. Much of the literature on meta and transfer learning in RL has focused on multi-”
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning (2022, positive): “Imitation learning has been successful in learning grasping and pick-place tasks from low52 dimensional state [9, 10, 11, 12, 13, 14, 15].”
Hierarchical Relative Entropy Policy Search (2014, background): “Alternative approaches for task generalization of parametrized options include learning a mixture of experts (Mülling et al., 2013) and Gaussian processes (Ude et al.”
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2018, background): “[66] trained a robot to play table tennis (2013).”
Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives (2019, methodology): “In [7], DMPs were modified to model fast movement inherent in hitting motion.”
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets (2017, background): “There has also been a separate line of work on learning from demonstration, which is then iteratively improved through reinforcement learning [17, 6, 21].”
Composing Complex Skills by Learning Transition Policies (2018, methodology): “In the robotics domain, Pastor et al. (2009); Kober et al. (2010); M¨ulling et al. (2013) have proposed a modular approach that learns table tennis by selecting appropriate low-level controllers.”
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale (2021, methodology): “Combining multiple task policies has been explored in reinforcement learning by using gating networks [76, 50], conditioning policies on tasks [15], mapping tasks to parameters of a policy [13, 38, 82], distilling separate task policies into a shared multi-task policy [40, 77, 62, 53, 27, 5].”
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data (2019, background): “Prior work has shown that robots can acquire a range of complex skills through demonstration, such as table tennis [1], drawer opening [2], and multi-stage manipulation tasks [3].”