Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that simulates articulated objects placed in real scenes. We then introduce SeqIK+$θ_0$, a fast and flexible representation for motion plans. Finally, we learn models that use SeqIK+$θ_0$ to quickly predict motion plans for articulating novel objects at test time. Experimental evaluation shows improved speed and accuracy at generating motion plans than pure search-based methods and pure learning methods.
Human locomotor control timescales depend on the environmental context and sensory input modality
Department of Electrical Engineering and Computer Science, School of Engineering, MIT（麻省理工学院电气工程与计算机科学系）；McGovern Institute for Brain Research, MIT（麻省理工学院麦戈文脑科学研究所）；Mechanical and Aerospace Engineering, Henry Samueli School of Engineering, UC Irvine（加州大学伊文斯顿分校机械与航空航天工程系）；Biomedical Engineering, Henry Samueli School of Engineering, UC Irvine（加州大学伊文斯顿分校生物医学工程系）；Ecology and Evolutionary Biology, School of Biological Sciences, UC Irvine（加州大学伊文斯顿分校生态与进化生物学系）
P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks
Learning Systems and Robotics Lab（学习系统与机器人实验室）；Technical University of Munich（慕尼黑技术大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Vector Institute for Artificial Intelligence（人工智能向量研究所）
PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University（J. Mike Walker ’66机械工程系，德克萨斯A&M大学）；Zachry Department of Civil and Environmental Engineering, Texas A&M University（Zachry土木与环境工程系，德克萨斯A&M大学）
