World modeling has emerged as an effective co-training objective for robot policies, giving rise to World Action Models (WAMs) that jointly predict actions and future states. However, most WAMs predict future states in the native representation space of pretrained visual backbones, resulting in high-dimensional targets...
Jie Chen, Ruofei Bai, Yu-Xin Cai et al.· 0 citations
Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperatively transport/manipulate a larger vessel. Collaborative pushing in this setting is challenging due to coupled hydrodynamics, low resistance, strong environmental dist...
Jun-Kai Lu, Jia-Dong Zhao, Jia-Cheng Zhang et al.· 0 citations
Autonomously constructing physically realizable 3D structures remains a significant challenge due to combinatorial action spaces, interchangeable components, equifinal assembly sequences, and strict stability requirements during construction. State-of-the-art methods fine-tune large language models for text-based gener...
Gadiel Sznaier Camps, Cheng-Yang He, G. Sartoretti et al.· 0 citations
RoboFoundry is proposed, the first embodied agentic framework that formulates this process of system-as-policy evolution across foundation models as Self-Evolving System-as-Policy, highlighting its potential for fully autonomous embodied agents.
Jing-Song Liang, Shu-Hao Liao, Shi-Zhe Zhang et al.· 0 citations
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-guided training, and PIBT-based action refinement. PRIMAL3 targets failures at topologically critical states, where agents must coordinate dec...
Cheng-Yang He, T. Duhan, Gadiel Sznaier Camps et al.· 1 citation
This work introduces an adaptive inference-time steering framework that leverages Reinforcement Learning on VLA Latents, and discovers that inference-time steering follows fundamentally different scaling laws under success and failure states, revealing that action diversity is most beneficial when the base VLA is likel...
Derek Ming Siang Tan, S. Shailesh, Srikrishna Iyer et al.· arXiv.org· 0 citations
Perceptive General Motion Tracking pipeline for humanoid robots that learns terrain adaptation from independently selected motion references and terrains, providing a unified policy for terrain-adaptive locomotion, diverse whole-body behaviors, and teleoperation in complex environments.
Hong-Yi Li, Pei-Zhuo Li, Yu-Cheng Tao et al.· 3 citations
CGFM-Nav is introduced, a foundation-model-based framework for lifelong multimodal navigation that integrates task-relevant subgraph selection, VLM reasoning, and verification feedback into a closed decision loop, and preliminary experiments show that CGFM-Nav improves the overall success rate, demonstrating the effect...
Yu-Xiang Xiao, Xibei Chen, Xin Zhou et al.· 0 citations
A Planner-Conditioned Diffusion Policy (PCDP) is proposed, trained on demonstrations from multiple planner styles with planner identity as an explicit conditioning input, enabling a single shared model to learn a multimodal trajectory distribution and generate diverse, controllable trajectory candidates from the same o...
This work introduces a unified RL formulation that jointly optimizes agent and environment policies, where the environment policy learns graph edge costs to provide global movement guidance via backward Dijkstra search and achieves significant improvements over the strong search-based planner, Causal-PIBT, across multi...
He Jiang, Jingtian Yan, Yulun Zhang et al.· 0 citations
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