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- FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation25
Surprisingly, the physical experiments show that the distilled force token outperforms direct sensor force measurements as well as other baselines, which highlights the effectiveness of this force-distilled VLA approach.
- DexGrasp-Diffusion: Diffusion-based Unified Functional Grasp Synthesis Method for Multi-Dexterous Robotic Hands25
The capacity of DexGrasp-Diffusion to reliably generate functional grasps for household objects aligned with specific affordance instructions is demonstrated, supporting the superior performance of MultiHandDiffuser over the baseline model in terms of success rate, grasp diversity, and collision depth.
- NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving1
Neural Token Reconstruction (NTR), a representation learning framework to directly constrain the compact scene-token bottleneck in perception-free driving, and introduces semantic priors derived from foundation-model annotations as a weak semantic interface biasing reconstruction targets toward driving-related structures without introducing explicit perception heads.
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- Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning–
This work proposes Binocular Alignment Learning (BAL), which could automatically learn the eye-hand coordination and points alignment capabilities to solve the four tasks and achieves a competitively good result with minimal cost on the four tasks.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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