Strategic Manipulation
Strategic manipulation encompasses the study of how agents, whether human or artificial, can influence systems or other agents to achieve desired outcomes. Current research focuses on developing methods to detect and mitigate manipulation in various contexts, including language models, robotic control, and multi-agent systems, often employing techniques like hierarchical planning, diffusion models, and transformer-based architectures. This field is crucial for building trustworthy AI systems and understanding human-computer interaction, with implications for improving the safety and robustness of robots and mitigating harmful biases in AI.
Papers
Analyzing Incentives and Fairness in Ordered Weighted Average for Facility Location Games
Kento Yoshida, Kei Kimura, Taiki Todo, Makoto Yokoo
DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment
Wendi Chen, Han Xue, Fangyuan Zhou, Yuan Fang, Cewu Lu
M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes
Sixu Yan, Zeyu Zhang, Muzhi Han, Zaijin Wang, Qi Xie, Zhitian Li, Zhehan Li, Hangxin Liu, Xinggang Wang, Song-Chun Zhu
Incorporating Task Progress Knowledge for Subgoal Generation in Robotic Manipulation through Image Edits
Xuhui Kang, Yen-Ling Kuo
Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task
Xiang Zhang, Hsien-Chung Lin, Yu Zhao, Masayoshi Tomizuka
PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation
Kaidong Zhang, Pengzhen Ren, Bingqian Lin, Junfan Lin, Shikui Ma, Hang Xu, Xiaodan Liang