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SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation
Jingkai Wang, Zihan Tang, Gu Zhang +7
Vision-language-action policies rely on large multimodal backbones to jointly perform perception, language conditioning, and action generation at every control step. Much of this c…
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
Yulin Luo, Chun-Kai Fan, Menghang Dong +19
Building robots that can perceive, reason, and act in dynamic, unstructured environments remains a central challenge. Recent embodied systems often follow a dual-system paradigm, w…
Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics
Enshen Zhou, Yibo Li, Jingkun An +12
Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metric-grounded reasoning compounded with complex spa…
FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation
Shuyi Zhang, Yunfan Lou, Hongyang Cheng +8
Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data. While Reinforcement Learning (RL) fine-tuning can surpass this limit…
Dexora: Open-source VLA for High-DoF Bimanual Dexterity
Zongzheng Zhang, Jingrui Pang, Zhuo Yang +22
Vision-Language-Action (VLA) models have recently become a central direction in embodied AI, but current systems are restricted to either dual-gripper control or single-arm dextero…
MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigation
Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu +7
Vision-and-language navigation (VLN) is a key task in Embodied AI, requiring agents to navigate diverse and unseen environments while following natural language instructions. Tradi…