8 papers
Compositional Physical Reasoning of Objects and Events from Videos
Zhenfang Chen, Shilong Dong, Kexin Yi +5
Understanding and reasoning about objects' physical properties in the natural world is a fundamental challenge in artificial intelligence. While some properties like colors and sha…
COMBO: Compositional World Models for Embodied Multi-Agent Cooperation
Hongxin Zhang, Zeyuan Wang, Qiushi Lyu +7
In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively pl…
Physically Compatible 3D Object Modeling from a Single Image
Minghao Guo, Bohan Wang, Pingchuan Ma +6
We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal…
Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble
Shun Zhang, Zhenfang Chen, Sunli Chen +3
Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values. However, RLHF relies on a reward model that is…
ContPhy: Continuum Physical Concept Learning and Reasoning from Videos
Zhicheng Zheng, Xin Yan, Zhenfang Chen +4
We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for assessing machine physical commonsense. ContPhy complements existing physical reasoning benchmarks by e…
SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge
Andong Wang, Bo Wu, Sunli Chen +5
Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks…