Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold. Scaling collection across diverse environments, in turn, favors lightweight, portable capture systems. Yet, existing datasets provide only partial views of th...
Yun-Song Zhou, Xue-Kun Jiang, Hai-Qi Shen et al.· 0 citations
Perceptual evaluations further show that training with AffectMoCap improves affect recognition from generated body motion, while real-robot experiments demonstrate continuous affect-conditioned behavior and stable long-horizon execution.
Cheng-Qun Yang, Teng-Jie Zhu, Liang Xu et al.· 0 citations
This work introduces InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences.
Ji-Song Cai, Yao Mu, Gan-Lin Yang et al.· 0 citations
Enfold is presented, which transfers this computation into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations: its future need not be materialized at every step if its internal structure can be enfolded into...
Wei-Li Zeng, Yi-Tong Xing, Fu-Long Liu et al.· arXiv.org· 0 citations
This work presents Enfold, which transfers this computation that constructs a future into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations if its internal structure can be enfolded into the present.
Wei-Li Zeng, Yi-Tong Xing, Fu-Long Liu et al.· 1 citation
InternVLA-A1.5 is presented, which builds the policy on a native VLM backbone that keeps training on VQA and subtask prediction, and attaches a lightweight unified expert for continuous action generation, and achieves the best overall results on all six simulation benchmarks.
This work presents REAL, an agentic framework for open-world mobile manipulation, which establishes sim-to-real-consistent environment APIs without oracle perception and integrates a simulated user to enable human-in-the-loop interaction.
Boyu Mi, Mengchen Ma, Yifei Yao et al.· arXiv.org· 2 citations
GaussianWAM is proposed, a training-time representation-enhancement framework that organizes geometric and semantic supervision through a 3D Gaussian field and improves performance on standard LIBERO and shows positive transfer trends on RoboTwin and real-world manipulation.
Zi-Jian Zhang, Yu-Qing Jiang, Wei-Tao Zhou et al.· 3 citations
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