Suchergebnisse - Computing methodologies Machine learning Learning settings Learning from demonstrations

VMP: Versatile Motion Priors for Robustly Tracking Motion on Physical Characters von Serifi, Agon, Grandia, Ruben, Knoop, Espen, Gross, Markus, Bächer, Moritz

“… Recent progress in physics‐based character control has made it possible to learn policies from unstructured motion data. However, it remains challenging to …”
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Learning to Move Like Professional Counter‐Strike Players von Durst, D., Xie, F., Sarukkai, V., Shacklett, B., Frosio, I., Tessler, C., Kim, J., Taylor, C., Bernstein, G., Choudhury, S., Hanrahan, P., Fatahalian, K.

“… In multiplayer, first‐person shooter games like Counter‐Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high‐level strategic …”
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Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data von Tappler, Martin, Pferscher, Andrea, Aichernig, Bernhard K., Konighofer, Bettina

“… Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL …”
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Preference-Conditioned Language-Guided Abstraction von Peng, Andi, Bobu, Andreea, Li, Belinda Z., Sumers, Theodore R., Sucholutsky, Ilia, Kumar, Nishanth, Griffiths, Thomas L., Shah, Julie A.

“… Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations …”
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Aligning Human and Robot Representations von Bobu, Andreea, Peng, Andi, Agrawal, Pulkit, Shah, Julie A., Dragan, Anca D.

“… they must be aligned. We observe that current learning approaches suffer from representation misalignment, where the robot's learned representation does not capture the human's representation …”
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Feature Expansive Reward Learning: Rethinking Human Input von Bobu, Andreea, Wiggert, Marius, Tomlin, Claire, Dragan, Anca D.

“… Our insight is that rather than implicitly learning about the missing feature(s) from demonstrations, the robot should instead ask for data that explicitly teaches it about what …”
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Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation von Chen, Letian, Paleja, Rohan, Ghuy, Muyleng, Gombolay, Matthew

“… On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations …”
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Enhancing Safety in Learning from Demonstration Algorithms via Control Barrier Function Shielding von Yang, Yue, Chen, Letian, Zaidi, Zulfiqar, van Waveren, Sanne, Krishna, Arjun, Gombolay, Matthew

“… Learning from Demonstration (LfD) is a powerful method for non-roboticists end-users to teach robots new tasks, enabling them to customize the robot behavior …”
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Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning von Trinh, Tu, Chen, Haoyu, Brown, Daniel S.

“… ? To address this problem, we propose a novel self-assessment approach based on Bayesian inverse reinforcement learning and value-at-risk, enabling learning-from-demonstration ("LfD …”
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Zero-Shot Learning to Enable Error Awareness in Data-Driven HRI von Ravishankar, Joshua, Doering, Malcolm, Kanda, Takayuki

“… Data-driven social imitation learning is a minimally-supervised approach to generating robot behaviors for human-robot interaction (HRI …”
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Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation von Daniele, Andrea F., Bansal, Mohit, Walter, Matthew R.

“… We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations …”
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Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision von Liao, Haiguang, Dong, Qingyi, Qi, Weiyi, Fallon, Elias, Kara, Levent Burak

“… In this work, we propose a machine learning driven method for solving the track-assignment detailed routing problem for advanced node analog circuits …”
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Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data von Nanavati, Amal, Doering, Malcolm, Brščić, Dražen, Kanda, Takayuki

“… Our proposed system for learning the interaction logic uses neural networks to first learn which customer actions are important to respond to and then learn how the shopkeeper should respond …”
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State-Aware Configuration Detection for Augmented Reality Step-by-Step Tutorials von Stanescu, Ana, Mohr, Peter, Kozinski, Mateusz, Mori, Shohei, Schmalstieg, Dieter, Kalkofen, Denis

“… Presenting tutorials in augmented reality is a compelling application area, but previous attempts have been limited to objects with only a small numbers of …”
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Sharing Model Framework for Zero‐Shot Sketch‐Based Image Retrieval von Ho, Yi‐Hsuan, Way, Der‐Lor, Shih, Zen‐Chung

“… Sketch‐based image retrieval (SBIR) is an emerging task in computer vision. Research interests have arisen in solving this problem under the realistic and challenging setting of zero‐shot learning …”
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Incremental learning of primitive skills from demonstration of a task von Sang Hyoung Lee, Hyung Kyu Kim, Il Hong Suh

“… In this work, we propose methods for automatically generating primitive skills from the demonstration of a task …”
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Encoding bi-manual coordination patterns from human demonstrations von Pais, Ana Lucia, Billard, Aude

“… hand configuration and grasp information. Secondly for learning the task we propose a method for extracting task constraints for each arm and coordination patterns between the arms …”
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Multi-thresholded approach to demonstration selection for interactive robot learning von Chernova, Sonia, Veloso, Manuela

“… Effective learning from demonstration techniques enable complex robot behaviors to be taught from a small number of demonstrations …”
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Enhancing Networked Embedded Systems Using Deep Learning Techniques von Asha, K, Kumar, Ritesh, Fiza, Samreen

“… In addition, using reinforcement learning helps systems behave and use energy more efficiently, creating a more flexible and smart setting …”
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Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction von Sufi, Fahim

“… This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT …”
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