9 papers
SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models
Md Kawsher Mahbub, Milon Biswas, Mirza Niaz Morshed +1
Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time. We introduce \textbf{SpatialUQ}, a post-…
RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
Tianxing Chen, Yue Chen, Zixuan Li +41
Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-hor…
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
Taowen Wang, Zikang Xie, Bin Yang +13
Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level decision-making and whole-body dyna…
A GPU-Parallel Framework for Heterogeneous Multi-Task Reinforcement Learning
Rui Zhang, Qiwei Wu, Zhengyu Zhang +7
GPU-parallel simulation provides abundant robot interaction, but existing benchmarks rarely combine this scale with heterogeneous manipulation tasks and standardized multi-task RL…
Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
Qiwei Wu, Rui Zhang, Xin Xiang +4
Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for ge…
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
Yunyang Mo, Jian Li, Qiwei Wu +2
While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe exploration. Human-in-the-l…
Morphology-Consistent Humanoid Interaction through Robot-Centric Video Synthesis
Weisheng Xu, Jian Li, Yi Gu +12
Equipping humanoid robots with versatile interaction skills typically requires either extensive policy training or explicit human-to-robot motion retargeting. However, learning-bas…
Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control
Weisheng Xu, Qiwei Wu, Jiaxi Zhang +7
Physics-based humanoid control relies on training with motion datasets that have diverse data distributions. However, the fixed difficulty distribution of datasets limits the perfo…
Gentle Manipulation Policy Learning via Demonstrations from VLM Planned Atomic Skills
Jiayu Zhou, Qiwei Wu, Jian Li +3
Autonomous execution of long-horizon, contact-rich manipulation tasks traditionally requires extensive real-world data and expert engineering, posing significant cost and scalabili…