Robot demonstration generation requires a system to identify where an interaction should occur, plan a feasible motion, and execute the required contact. HiWE connects these decisions through a point-based interface between visual grounding and language-based planning. PointVLM is instruction-tuned to associate task-re...
Guo-Qing Ma, Ming-Qi Yuan, Chen Gao et al.· 0 citations
Real-world driving is inherently multi-agent, yet most existing driving world models generate observations from a single ego vehicle. Independently extending them to multiple vehicles does not ensure that different agents observe a consistent shared world. We present CoDrive, a cross-vehicle, multi-view driving video g...
Yu Meng, Bai-Ning Zhao, Jun-Tao Wu et al.· 0 citations
This work introduces Anchored Planning, which retrieves a recorded segment whose start and end resemble the current and goal observations, then aims at an observation shortly after its start, and without additional training, planning toward observed targets outperforms the LeWM planner on every task in the authors' lon...
Xvyuan Liu, Jian-Jie Fang, Wei Wu et al.· 0 citations
IMPACT is introduced, a scalable Interaction-aware Model training framework with Prior-guided Attention Calibration and Targeting, which consistently outperforms the corresponding MSE-trained baselines, improving interaction fidelity, physical plausibility, and visual quality.
Rong-Ze Tang, Jian-Jie Fang, Zhao-Lu Wang et al.· 1 citation
Causal Action Effect Reweighting (CAER), a general training paradigm that redistributes supervision toward the tokens whose predicted future is causally affected by the action, is introduced.
Jian-Jie Fang, Xvyuan Liu, Zi-You Wang et al.· 0 citations
The first benchmark to bridge cyberspace reasoning and physical-world interaction for UAV embodied perception is introduced, and ActiveFly, a closed-loop UAV agent that integrates visual-language reasoning with fine-grained control, is developed and deployed on a physical UAV platform.
WorldScape Policy 2.0 is introduced, a controllable WAM with reasoning-augmented long short-term memory and fine-grained instruction following and in-context adaptation that demonstrates superior capabilities in long-horizon autonomous planning, fine-grained instruction following and in-context adaptation.
Hai-Sheng Su, Zong-Dai Liu, Xin Jin et al.· arXiv.org· 2 citations
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