Výsledky vyhledávání - "Computing methodologies Machine learning Learning settings Learning from demonstrations"

VMP: Versatile Motion Priors for Robustly Tracking Motion on Physical Characters Autor 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 Autor 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 Autor 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) by learning from…”
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Feature Expansive Reward Learning: Rethinking Human Input Autor Bobu, Andreea, Wiggert, Marius, Tomlin, Claire, Dragan, Anca D.

“…When a person is not satisfied with how a robot performs a task, they can intervene to correct it. Reward learning methods enable the robot to adapt its reward…”
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Aligning Human and Robot Representations Autor Bobu, Andreea, Peng, Andi, Agrawal, Pulkit, Shah, Julie A., Dragan, Anca D.

“…To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or…”
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Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation Autor Chen, Letian, Paleja, Rohan, Ghuy, Muyleng, Gombolay, Matthew

“…Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the…”
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Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning Autor Trinh, Tu, Chen, Haoyu, Brown, Daniel S.

“…We examine the problem of determining demonstration sufficiency: how can a robot self-assess whether it has received enough demonstrations from an expert to…”
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Enhancing Safety in Learning from Demonstration Algorithms via Control Barrier Function Shielding Autor 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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Zero-Shot Learning to Enable Error Awareness in Data-Driven HRI Autor 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). However, this type of…”
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Preference-Conditioned Language-Guided Abstraction Autor 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. Recent work constructs state…”
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Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation Autor Daniele, Andrea F., Bansal, Mohit, Walter, Matthew R.

“…Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural…”
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Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision Autor Liao, Haiguang, Dong, Qingyi, Qi, Weiyi, Fallon, Elias, Kara, Levent Burak

“…Detailed routing is one of the most critical steps in analog circuit design. Complete routing has become increasingly more challenging in advanced node analog…”
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Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data Autor Nanavati, Amal, Doering, Malcolm, Brščić, Dražen, Kanda, Takayuki

“…We envision a future where service robots autonomously learn how to interact with humans directly from human-human interaction data, without any manual…”
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State-Aware Configuration Detection for Augmented Reality Step-by-Step Tutorials Autor 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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