Ai alignment: A comprehensive survey
AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …
[HTML][HTML] State transition learning with limited data for safe control of switched nonlinear systems
Switching dynamics are prevalent in real-world systems, arising from either intrinsic changes
or responses to external influences, which can be appropriately modeled by switched …
or responses to external influences, which can be appropriately modeled by switched …
Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization
Data is crucial for robotic manipulation, as it underpins the development of robotic systems
for complex tasks. While high-quality, diverse datasets enhance the performance and …
for complex tasks. While high-quality, diverse datasets enhance the performance and …
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies
This letter presents our solution for the Real Robot Challenge III 1, aiming to address
dexterous robotic manipulation tasks through learning from offline data. In this competition …
dexterous robotic manipulation tasks through learning from offline data. In this competition …
Dataset Clustering for Improved Offline Policy Learning
Q Wang, Y Deng, FR Sanchez, K Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
Offline policy learning aims to discover decision-making policies from previously-collected
datasets without additional online interactions with the environment. As the training dataset …
datasets without additional online interactions with the environment. As the training dataset …
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains
In this paper, we investigate an offline reinforcement learning (RL) problem where datasets
are collected from two domains. In this scenario, having datasets with domain labels …
are collected from two domains. In this scenario, having datasets with domain labels …
Predicting Long-Term Human Behaviors in Discrete Representations via Physics-Guided Diffusion
Long-term human trajectory prediction is a challenging yet critical task in robotics and
autonomous systems. Prior work that studied how to predict accurate short-term human …
autonomous systems. Prior work that studied how to predict accurate short-term human …