Learning from Demonstrations Through the Use of Non-rigid Registration

By John Schulman, Jonathan Ho, Cameron Lee, Pieter Abbeel. 2013

162 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning (2017, background): “Recent results have shown success on games (Mnih et al. (2013)), locomotion (Schulman et al. (2015)), and a variety of robotic manipulation skills (Pinto & Gupta (2017); Schulman et al. (2016); Gu et al. (2017)).”
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data (2019, methodology): “The loss function for the RNN is a simple Behavioral Cloning loss L θ ( a t : t + T , s t : t + T .”
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2017, neutral): “It has been applied successfully to a wide range of domains in robotics, for example to autonomous driving [4], [5], [6], autonomous helicopter ﬂight [7], gesturing [8], and manipulation [9], [10].”
An Algorithmic Perspective on Imitation Learning (2018, methodology): “Way points with non-rigid registration [Schulman et al., 2013] A point cloud of the given scene Generalization based on point clouds of a given scene Stochasticity of demonstrations is not considered”
DART: Noise Injection for Robust Imitation Learning (2017, background): “Deep reinforcement learning has the potential to train such control policies, however in practice it may require a very large number of samples [18].”
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations (2016, methodology): “used HC LfD for rope tying using kernelized interpolation as the policy class [24].”
Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot (2017, methodology): “Schulman et al. [39] used Thin Plate Splines to warp kinesthetically demonstrated trajectories of rope tying with a PR2 to solve novel test cases.”
Generalized Cylinders for Learning, Reproduction, Generalization, and Refinement of Robot Skills (2017, background): “[25] demonstrated the usefulness of nonrigid registration in LfD by employing it for autonomous knot tying.”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “, the tip of a tool [99]), or use nonrigid registration or geometry warping to determine correspondences of entire sets of points across task instances [338, 156, 361, 315, 12].”
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks (2020, background): “In recent years, learning-based approaches for manipulation have grown in popularity to improve the generalization of knot-tying and also to manipulate rope towards general target configurations [52], [46], [49], [57], [64], [61].”
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data (2020, background): “There is also extensive literature on deformable manipulation [18, 25, 27, 36, 40].”
An Introduction to Deep Reinforcement Learning (2018, unknown): “Thus, transfer learning may also be of crucial importance (Schulman et al., 2016; Liu et al., 2017).”
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation (2021, methodology): “Template-matching with coarse 3D primitives [15, 23, 42] or non-rigid registration [36] can”
Movement primitives via optimization (2015, methodology): “Among several tools for addressing this problem [1, 4–6], a commonly used one is a Dynamic Movement Primitive (DMP) [7, 8].”
Learning legged swimming gaits from experience (2015, unknown): “This choice is inspired by authors such as Xu et al. [15], [16] who have recently shown that human guidance, in the forms of demonstrations or direct participation, are effective aids for learning practical robotic tasks.”
Modeling, learning, perception, and control methods for deformable object manipulation (2021, background): “Subsequent research generalizes motions for configurations that are very different from demonstrations (13, 108) and connect deformable shape registration to adaptation of demonstrated trajectories.”