1 citations · 3 across the 40 of their papers we have counts for
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LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models
Qixin Sun, WenLong Gao, Xiansheng Chen +3
Robotic manipulation often requires remembering interactions that are no longer visible. We introduce Latent Transition Memory (LTM), which stores history as compact latent transit…
HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation
Naisheng Ye, Yinzhe Zhou, Junkai Zhao +5
Contact-rich dexterous manipulation requires policies that translate physical feedback into motion commands while regulating interaction loads across evolving multi-contact interac…
DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination
Yankai Fu, Ning Chen, Junkai Zhao +5
Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-language-action (VLA) models d…
PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment
Yuyang Liu, Yanqing Shen, Ruike Chen +22
Fine-grained robotic evaluation matters for understanding embodied models, going beyond binary success rates and rule-based process scores. We present PRM-as-a-Judge 1.5, a toolkit…
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…
Orca: The World is in Your Mind
Yihao Wang, Yuheng Ji, Mingyu Cao +54
We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals and exposes it through multi…
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…
LAST: Bridging Vision-Language and Action Manifolds via Gromov-Wasserstein Alignment
Huaihai Lyu, Chaofan Chen, Yuheng Ji +4
We take a Gromov-Wasserstein perspective on Vision-Language-Action (VLA) learning, where the goal is to make the relational geometry of action representations compatible with the s…
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…
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9
World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However, standard approaches often fo…
PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing
Yuheng Ji, Yuyang Liu, Huajie Tan +15
Current robotic evaluation is still largely dominated by binary success rates, which collapse rich execution processes into a single outcome and obscure critical qualities such as…
SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics
Mengzhen Liu, Enshen Zhou, Cheng Chi +6
Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoi…
AoE: Always-on Egocentric Human Video Collection for Embodied AI
Bowen Yang, Zishuo Li, Yang Sun +15
Embodied foundation models require large-scale, high-quality real-world interaction data for pre-training and scaling. However, existing data collection methods suffer from high in…
AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception
Ruoxuan Feng, Yuxuan Zhou, Siyu Mei +6
Real-world contact-rich manipulation demands robots to perceive temporal tactile feedback, capture subtle surface deformations, and reason about object properties as well as force…
Reshaping Action Error Distributions for Reliable Vision-Language-Action Models
Shuanghao Bai, Dakai Wang, Cheng Chi +8
In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies. Most existing VLA framewor…
Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models
Shuanghao Bai, Jing Lyu, Wanqi Zhou +9
Vision-Language-Action (VLA) models benefit from chain-of-thought (CoT) reasoning, but existing approaches incur high inference overhead and rely on discrete reasoning representati…
RoboBrain 2.5: Depth in Sight, Time in Mind
Huajie Tan, Enshen Zhou, Zhiyu Li +32
We introduce RoboBrain 2.5, a next-generation embodied AI foundation model that advances general perception, spatial reasoning, and temporal modeling through extensive training on…
Action-Sketcher: From Reasoning to Action via Visual Sketches for Long-Horizon Robotic Manipulation
Huajie Tan, Peterson Co, Yijie Xu +9
Long-horizon robotic manipulation is increasingly important for real-world deployment, requiring spatial disambiguation in complex layouts and temporal resilience under dynamic int…
Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control
Zhe Li, Cheng Chi, Yangyang Wei +9
Humans intuitively move to sound, but current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands. Generating motion fro…