Sep 2026· IEEE Robotics and Automation Letters· Vol 11, pp. 10114-10121· 0 citations· 49 references
Computer Science
Abstract
3D imitation learning has demonstrated capability in diverse visuomotor tasks but often struggles with small objects or high-precision manipulation due to geometric sparsity. To address this, we propose the Multimodal Object-aware Policy (MOP), a novel framework that incorporates a geometry-guided fusion module to adaptively integrate 2D semantic features with 3D geometry for precise control. Additionally, we introduce a lightweight Object Position Prediction (OPP) module that serves as an auxiliary supervision signal. The training labels for this module are generated by using a cost-effective vision-based software tracker, effectively replacing expensive hardware-based motion capture systems. We evaluate our policy across 56 tasks on 3 simulation benchmarks. Experimental results demonstrate that MOP significantly outperforms baselines, achieving higher success rates with lower variance and efficient inference. Real-world experiments on 4 tasks further validate the robustness and transferability of our approach.
A lightweight retargeting pipeline that kinematically retargets human interaction data onto six-degree-of-freedom manipulator trajectories to fine-tune policies based on the pi0.5 architecture is proposed and evaluations under extreme visual clutter reveal that explicitly retargeted policies exhibit immunity to semanti...
José António Nunes Andrade, Mauro Castelli· Decision Making Advances· 0 citations
Two systematic attempts to improve large pretrained models with minimal or zero modification to their weights via reinforcement learning on a frozen OpenVLA-7B using binary task-success rewards on LIBERO-Goal reveal a common ceiling.
A Movement Primitive learning algorithm based on Gaussian Processes, combined with real-time zero-shot adaptation through Pathwise Conditioning, which encapsulates the predictive uncertainty of the demonstrated movement using heteroscedastic GPs and utilizes an update via Matheron's rule to instantaneously adjust the t...
Adrián Prados, L. Lishan, Alberto Mendez et al.· Jornadas de Automática· 0 citations
The core insight is that a retrieved anchor affordance, which captures the desired contact point between the end-effector and the target object, can be propagated forward via affordance propagation, to yield an affordance trajectory that provides temporally consistent, state-aware guidance throughout execution.
Gao-Yuan Wu, Ziyu Shan, Hao-Yang Du et al.· 0 citations
GaussianDream demonstrates that training-time current Gaussian reconstruction and future Gaussian prediction provide effective 3D supervision, but its dense VGGT/TGE-based prefix jointly carries state, dynamics, and action-conditioning information.
Yu-Qing Jiang, Zi-Jian Zhang, Wei-Tao Zhou et al.· 1 citation
Cross-embodiment navigation is a key challenge in embodied intelligence. Due to differences in embodiment, the same visual observation may imply different actions for different agents, making prediction ambiguous when relying solely on vision. Existing studies mainly rely on reinforcement learning, which requires large...
Jialu Zhang, Yong Du, Xianda Guo et al.· arXiv.org· 0 citations
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