Most robot policies, whether trained by imitation learning (IL) or reinforcement learning (RL), map observations to action values at discrete control steps and leave the continuity of physical motion to be learned implicitly. We propose ODE policy, which learns the dynamics of the action rather than the action itself....
Jin-Dou Jia, Jing-Liang Li, Kuang-Ji Zuo et al.· 0 citations
Differences in hand geometry, joint parameterization, and kinematic topology pose challenges for cross-embodiment dexterous grasp generation. However, spatial relations among links alone are insufficient to fully capture the hand's kinematic topology. We propose DexGraspFlow, a conditional link-level $\mathrm{SE}(3)$ f...
Bin-Jia Zhou, Bo-Yu Ma, Xin-Yu Zhou et al.· 0 citations
A demonstration-based robotic feeding framework that integrates human demonstration, Dynamic Movement Primitive-based motion planning, vision-based scene perception, and robot control is presented and it is shown that the learned spoon motions can be reproduced within the detected experimental scene.
Kenzhi Iskandar Wong, Kitti Thamrongaphichartkul, Sri Harsha Turlapati et al.· 0 citations
Vision-and-Language Navigation (VLN) requires embodied agents to ground language in visual observations and make long-horizon decisions. Existing methods often address these demands through task-specific architectures. As multimodal large language models (MLLMs) become increasingly capable, an important question remain...
Ling-Yi Hong, Wei-Ren Cai, Jia-Yu Chen et al.· 0 citations
RoboDreamer is presented, a two-stage teacher--student framework that combines next-observation consistency with randomized continuous temporal masking that demonstrates robust motion tracking under observation masking and successful real-world deployment.
Zhe Li, Yang-Yang Wei, Xi-Chen Yuan et al.· 0 citations
Vision-Language-Action (VLA) models are typically deployed as isolated single-agent policies. When several such agents share a workspace, they can coordinate implicitly, by observing one another, or explicitly, by exchanging messages, but it remains unclear when explicit communication becomes necessary. We introduce Co...
Ahaskar Kashyap, Jia-Wen Zong, Bo-Yu Ma et al.· 0 citations
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and conditioning across reward definitions and label granularities using 5,000 naturally suboptimal human demonstrations and 24 target tasks. Wi...
Ming-Xuan Yan, Li-Tian Gong, Di Huang et al.· 0 citations
Online control of execution speed is essential for deploying robot policies in real-world scenarios, as robots may need to speed up under time constraints or slow down to facilitate human interaction and improve safety. However, imitation-learned policies inherit the execution speed of their demonstrations, and test-ti...
Yuxuan Hu, Shilin Shan, Qi-Heng Wang et al.· 0 citations
Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object delivery. However, most existing HRI systems rely on RGB cameras that continuously observe humans to respond to non-verbal commands, such as hand gestures. This raises privac...
Jun-Qiao Fan, Yuxuan Hu, Bofan Lyu et al.· 0 citations
Hierarchical vision-language-action (VLA) systems consist of a high-level vision-language planner and a low-level action expert that generates continuous actions. This hierarchical design has practical value only if the planner can generate plans fast enough to meet real-time control requirements, and the resulting pla...
Chuan-Liang Xie, Bo-Yu Ma, Gen Li et al.· 0 citations
Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previous scene. Many simulation robustness benchmarks fix external conditions at reset, leaving this temporal challenge underexamined. We introdu...
Yun-Bei Zhang, Zi-Jian Jin, Yuan-Zhe Liu et al.· 0 citations
Failure for Rising is proposed, a failure-driven real-to-sim-to-real closed-loop learning framework that converts real-world failures into targeted policy improvement and reconstructs each failure as an interactive, object-centric table-top environment that preserves the task-relevant spatial and physical conditions.
Zhuo-Yuan Yu, Jia-Cheng Wang, Tian-Le Liu et al.· 0 citations
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