1 citations · 3 across the 20 of their papers we have counts for
22 papers
TRACE: Efficient Transfer Learning for Neural Whole-Body Control Across Humanoid Embodiments
Qingyu Lan, Zhihao Tian, Zifan Wang +1
Humanoid behavior foundation models (BFMs) enable diverse whole-body control, but adapting a pretrained controller to a new embodiment remains challenging because of kinematic and…
Latent-Space Importance Weighting under Covariate Shift
Yuling Jiao, Lican Kang, Yang Xiang +2
In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feature of our approach is the use of…
TemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation
Jiarui Yang, Yehao Lu, Yuning Su +9
Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not reliably capture recent phys…
Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization
Jiaming Zhou, Qihang Zhang, Gangwei Xu +9
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language mod…
Multi-View Unified Camera Fields: Geometry-Shaped Action-Facing Representations for RGB-Only Multi-Camera VLA Policies
Jiarui Yang, Yehao Lu, Yuning Su +9
Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation, yet complex contact-rich tasks often benefit from multi-camera observations that joint…
Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
Zifan Wang, Yizhao Li, Teli Ma +5
Humanoid behavior foundation models aim to acquire reusable whole-body control policies from broad human motion priors, enabling a single controller to produce diverse and expressi…
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
Jia Zheng, Teli Ma, Yudong Fan +3
World Action Models (WAMs) couple a video dynamics prior to the policy and have shown encouraging results on tabletop manipulation, but iterative denoising over high-dimensional vi…
DiT4DiT: Jointly Modeling Video Dynamics and Actions for Generalizable Robot Control
Teli Ma, Jia Zheng, Zifan Wang +4
Vision-Language-Action (VLA) models have emerged as a promising paradigm for robot learning, but their representations are still largely inherited from static image-text pretrainin…
From Watch to Imagine: Steering Long-horizon Manipulation via Human Demonstration and Future Envisionment
Ke Ye, Jiaming Zhou, Yuanfeng Qiu +4
Generalizing to long-horizon manipulation tasks in a zero-shot setting remains a central challenge in robotics. Current multimodal foundation based approaches, despite their capabi…
EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations
Jiayi Liu, Jiaming Zhou, Ke Ye +3
Reliable trajectory prediction from an ego-centric perspective is crucial for robotic navigation in human-centric environments. However, existing methods typically assume noiseless…
End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
Zifan Wang, Xun Yang, Jianzhuang Zhao +5
The deployment of humanoid robots in unstructured, human-centric environments requires navigation capabilities that extend beyond simple locomotion to include robust perception, pr…
GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation
Teli Ma, Jia Zheng, Zifan Wang +3
Learning manipulation skills from human demonstration videos offers a promising path toward generalizable and interpretable robotic intelligence-particularly through the lens of ac…
Omni-Perception: Omnidirectional Collision Avoidance for Legged Locomotion in Dynamic Environments
Zifan Wang, Teli Ma, Yufei Jia +5
Agile locomotion in complex 3D environments requires robust spatial awareness to safely avoid diverse obstacles such as aerial clutter, uneven terrain, and dynamic agents. Depth-ba…
Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization
Jiaming Zhou, Ke Ye, Jiayi Liu +6
The generalization capabilities of vision-language-action (VLA) models to unseen tasks are crucial to achieving general-purpose robotic manipulation in open-world settings. However…
GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping
Teli Ma, Zifan Wang, Jiaming Zhou +2
Inferring affordable (i.e., graspable) parts of arbitrary objects based on human specifications is essential for robots advancing toward open-vocabulary manipulation. Current grasp…
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
Teli Ma, Jiaming Zhou, Zifan Wang +2
Developing robots capable of executing various manipulation tasks, guided by natural language instructions and visual observations of intricate real-world environments, remains a s…
Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation
Jiaming Zhou, Teli Ma, Kun-Yu Lin +3
Learning generalizable visual representations across different embodied environments is essential for effective robotic manipulation in real-world scenarios. However, the limited s…
Prioritized Semantic Learning for Zero-shot Instance Navigation
Xinyu Sun, Lizhao Liu, Hongyan Zhi +2
We study zero-shot instance navigation, in which the agent navigates to a specific object without using object annotations for training. Previous object navigation approaches apply…
ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition
Jiaming Zhou, Junwei Liang, Kun-Yu Lin +2
Zero-shot action recognition (ZSAR) aims to learn an alignment model between videos and class descriptions of seen actions that is transferable to unseen actions. The text queries…
GeoDeformer: Geometric Deformable Transformer for Action Recognition
Jinhui Ye, Jiaming Zhou, Hui Xiong +1
Vision transformers have recently emerged as an effective alternative to convolutional networks for action recognition. However, vision transformers still struggle with geometric v…
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition
Jiaming Zhou, Hanjun Li, Kun-Yu Lin +1
Developing end-to-end action recognition models on long videos is fundamental and crucial for long-video action understanding. Due to the unaffordable cost of end-to-end training o…
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
Rong Li, ShiJie Li, Xieyuanli Chen +3
LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist wi…