Search Results - Computing methodologies Machine learning Learning settings Learning from demonstrations

VMP: Versatile Motion Priors for Robustly Tracking Motion on Physical Characters by 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…”
Get full text

Learning to Move Like Professional Counter‐Strike Players by 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…”
Get full text

Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data by 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…”
Get full text

Preference-Conditioned Language-Guided Abstraction by 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…”
Get full text

Aligning Human and Robot Representations by 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…”
Get full text

Feature Expansive Reward Learning: Rethinking Human Input by 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…”
Get full text

Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation by 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…”
Get full text

Enhancing Safety in Learning from Demonstration Algorithms via Control Barrier Function Shielding by 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…”
Get full text

Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning by 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…”
Get full text

Zero-Shot Learning to Enable Error Awareness in Data-Driven HRI by 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…”
Get full text

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation by 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…”
Get full text

Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision by 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…”
Get full text

Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data by 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…”
Get full text

State-Aware Configuration Detection for Augmented Reality Step-by-Step Tutorials by 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…”
Get full text

Sharing Model Framework for Zero‐Shot Sketch‐Based Image Retrieval by 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…”
Get full text

Incremental learning of primitive skills from demonstration of a task by 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…”
Get full text

Encoding bi-manual coordination patterns from human demonstrations by 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…”
Get full text

Multi-thresholded approach to demonstration selection for interactive robot learning by Chernova, Sonia, Veloso, Manuela

“…Effective learning from demonstration techniques enable complex robot behaviors to be taught from a small number of demonstrations…”
Get full text

Enhancing Networked Embedded Systems Using Deep Learning Techniques by 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…”
Get full text

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction by 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…”
Get full text