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Showing 1 to 20 of 62 for “"Imitation learning"”.
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Robust imitation learning from observation
Imitation learning, sometimes referred as learning from demonstrations, has been used in real world scenarios because of its sample efficiency and computational feasibility, such as autonomous driving and robotics control. However, imitation learning often suffers from compounding error and data …
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Imitation learning of whole-body grasps
… people. In much the same way, robots can use imitation learning to pick up useful skills. A system is demonstrated here for using imitation learning to teach a robot to grasp objects using both hand and whole-body grasps, which use the arms and torso as well as hands. Demonstration grasp …
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Teaching Robots using Interactive Imitation Learning
… applications, the need for adaptable and robust learning methods becomes paramount. In this dissertation we develop Interactive Imitation Learning (IIL) based methods that allow robots to learn from imperfect demonstrations. We achieve this by incorporating human factors such as the quality of …
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Optimal Strategy Imitation Learning from Differential Games
… continuous control policy, we use deep imitation learning to model the discrete approximation of a differential game solution. We successfully learn the policy generated for two games of different complexity, a fence escape and merging game, and show that the imitated policy generates …
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Deep learning based approaches for imitation learning.
Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation …
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Subdominance Minimization: A Satisficing Perspective on Imitation Learning
… conflicting objectives. However, prevailing imitation learning methods tend to prioritize optimizing a single imitation objective. This myopic focus on a singular objective frequently leads to unintended and undesirable behaviors in learned models. For example, an autonomous vehicle …
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Imitation learning through games: theory, implementation and evaluation
… the potential offered by modern machine-learning approaches, though games which actually implement said approaches on more than a token scale remain scarce. One area which has not yet received much attention from either academia or industry is imitation learning, which seeks to expedite …
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Engineering Design Automation via Imitation Learning and Reinforcement Learning
Reinforcement Learning (RL) has achieved notable success in robotics and gaming, yet its application to automating engineering design faces significant challenges, including slow training times and poor generalization. Traditional RL methods require exploring millions of design states, which is …
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Can oracle-based imitation learning improveneural machine translation with dataaggregation?
… translation(MT). A lot of focus in machine learning is going towards the research of alternativelearning techniques, as the basic existing paradigms such as supervised, unsupervised andreinforcement learning are not a perfect fit for every task. Imitation learning is a techniquewhich …
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Imitation Learning for Sequential Manipulation Tasks: Leveraging Language and Perception
… well to new tasks or contexts. In addition, learning an end-to-end policy for performing a sequence of operations for a high-level goal remains a challenge. Transferring sequential task specifications is a difficult objective, as it requires extensive human intervention to establish the …
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Optimizing crop management with reinforcement learning, imitation learning, and crop simulations
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Imitation Learning with Superhuman Policy Gradient Optimization for Sequential Cancer Treatment Decisions
We propose a simulator-driven imitation learning framework for sequential deci- sion making in head and neck cancer (HNC) treatment. Our method, Superhu- man Policy Gradient Optimization (SPGO), integrates inverse reinforcement learning principles with policy gradient updates to derive three-stage …
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Efficient Imitation Learning for Robust, Adaptive, Vision-based Agile Flight Under Uncertainty
… and Power (CSWaP)constraints. The existing imitation learning strategies that can train computationally efficient deep neural network policies from those algorithms have limited robustness and/or are impractical (large number of demonstrations, training time), limiting rapid policy learning …
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On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control
Recent work in imitation learning has shown that having an expert controller that is both suitably smooth and stable enables much stronger guarantees on the performance of the approximating learned controller. Constructing such smoothed expert controllers for arbitrary systems remains challenging, …
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Design and Evaluation of Skill-Based Imitation Learning Policies for Robotic Manipulation
Imitation learning is a popular approach for obtaining intelligent robotic policies by learning from human demonstrations. Within this field, there is significant interest in the development of multi-task architectures that can efficiently learn diverse sets of tasks. Skill-based imitation learning …
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Songbird Brain Organization and its Molecular Convergence with Humans for Vocal Imitation Learning
… on our understanding of the evolution of vocal imitation learning across species. Much is still unknown about the number and organization of unique neural cell populations in the avian brain. The avian dorsal and ventral pallium, separated by a vestigial ventricle divide, each contain brain …
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The Contribution of Planning-related Motor Processes to Mental Practice and Imitation Learning
… Experiment 5 and 6 then extend these results to imitation learning. Participants were instructed to learn the rhythms by observing somebody else, while again making unrelated responses with their hand and feet. While previous work on stimulus response compatibility focussed on testing automatic …
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Robot See, Robot Do: On the Development of Robust and Adaptive Imitation Learning for Robots
… and unseen circumstances becomes crucial. Imitation learning offers a promising paradigm for robots to learn complex tasks by mimicking human behavior. However, traditional imitation learning approaches face key challenges in integrating diverse feedback types, managing noisy and …
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Force Feedback and Tactile Sensing for Robotic Teleoperation of Contact Rich Manipulation Tasks
Imitation learning has shown promising results in teaching robots new skills. We propose augmenting the ALOHA bimanual teleoperation system with haptic feedback to obtain higher quality expert demonstrations. We add two types of haptic feedback: force feedback and cutaneous feedback in both a real …
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Sensorimotor Processes Underpinning Imitaiton Learning of Biological Motion
… biological motion is coded and imitated during imitation learning by improving upon methodologies currently used in the literature to examine imitation of underlying movement kinematics. Across four experiments, imitation of the kinematic structures of biological and non-biological motion models …
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