9 citations · 9 across the 6 of their papers we have counts for
8 papers
What Makes a Good Symbol for Hybrid Discrete-Continuous Planning?
Yiyuan Pan, Changliu Liu, Jiayuan Mao
The physical world has continuous state and action spaces, yet robot planning often benefits from hybrid discrete-continuous formulations that explicitly expose combinatorial struc…
Understanding and Mitigating the Video-Action Generalization Gap via Temporal Ratio
Utkarsh A. Mishra, Yongxin Chen, Danfei Xu +3
Generative video foundation models exhibit strong compositional priors, yet world-action models (WAMs) and video-action models (VAMs) often lose these priors after finetuning on ro…
ForceBand: Learning Forceful Manipulation with sEMG
Botao He, Zhi Wang, Linna Kuang +8
Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as motion-capture trajectories a…
Adversarial Game-Theoretic Algorithm for Dexterous Grasp Synthesis
Yu Chen, Botao He, Yuemin Mao +7
For many complex tasks, multi-finger robot hands are poised to revolutionize how we interact with the world, but reliably grasping objects remains a significant challenge. We focus…
Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models
Simeng Han, Howard Dai, Stephen Xia +7
Accuracy remains a standard metric for evaluating AI systems, but it offers limited insight into how models arrive at their solutions. In this work, we introduce a benchmark based…
Agent Workflow Memory
Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried +1
Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action tr…
Infer Human's Intentions Before Following Natural Language Instructions
Yanming Wan, Yue Wu, Yiping Wang +2
For AI agents to be helpful to humans, they should be able to follow natural language instructions to complete everyday cooperative tasks in human environments. However, real human…
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
Yanwei Wang, Tsun-Hsuan Wang, Jiayuan Mao +2
Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI. Whereas prior works have focused on…