11 papers
Spark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manipulation
Tianxing Chen, Wenwei Lin, Chuqiao Lyu +19
Generalist robot policies benefit from large-scale visual pretraining but often struggle with contact-rich dexterous manipulation, where force, compliance, and incipient slip are h…
GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
Qize Yu, Lianrui Fan, Bowen Ping +19
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidenc…
MM-ABC: Towards Generalist Mobile Manipulation via Seeing, Coordinating and Imagining
Qiwei Liang, Guangyu Chen, Shaolong Zhu +6
Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable. This flexibility introduces two central challen…
An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond
Wenbo Zhang, Kaixuan Wang, Yutao Ouyang +9
Embodied AI systems are often organized into System 1 and System 2. System 1 is typically a pretrained policy that generates actions at high frequency, whereas System 2 is often in…
BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion
Chenxin Liang, Youchen Lai, Chuqiao Lyu +3
Bimanual interaction produces complementary tactile views of the same physical process, yet existing tactile representation learning largely models the two hands independently or c…
DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
Yan Qin, Yue Chen, Wenwei Lin +9
Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots and remain tied to embodiment…
TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation
Danyan Zhou, Jinxuan Lu, Jiawei Lin +3
Tactile signals provide direct contact and force measurements that are essential for understanding physical interactions and enabling dexterous robotic manipulation. However, tacti…
CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations
Huan-ang Gao, Zikang Zhang, Tianwei Luo +9
Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robu…
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Haozhan Li, Yuxin Zuo, Jiale Yu +18
Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and super…
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World
Yankai Fu, Qiuxuan Feng, Ning Chen +8
Achieving human-level dexterity in robots is a key objective in the field of robotic manipulation. Recent advancements in 3D-based imitation learning have shown promising results,…
ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation
Zifeng Gao, Guanxing Lu, Tianxing Chen +5
Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent diffusion-based methods show impressive p…