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- VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback70
This work introduces two key innovations: a pipeline that leverages a pretrained tactile-language model that provides semantic tactile feedback for high-level task planning, and a diffusion-based controller that refines VLA-generated actions with tactile signals for contact-rich manipulation.
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- Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning18
The experiments show that Skill-Critic's low-level policy fine-tuning and demonstration-guided regularization are essential for good performance, and the proposed Skill-Critic algorithm optimizes both the low-level and high-level policies.
- UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models15
This work proposes a comprehensive paradigm, termed UniAff, that integrates 3D object-centric manipulation and task understanding in a unified formulation and constructed a dataset labeled with manipulation-related key attributes, comprising 900 articulated objects and 600 tools from 12 categories.
- VFP: Variational Flow-Matching Policy for Multi-Modal Robot Manipulation12
The Variational Flow-Matching Policy (VFP), which introduces a variational latent prior for mode-aware action generation and effectively captures both task-level and trajectory-level multi-modality, is proposed.
- BeTAIL: Behavior Transformer Adversarial Imitation Learning From Human Racing Gameplay10
This work proposes BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer policy from human demonstrations with online AIL to model the sequential decision-making process of human experts and correct for out-of-distribution states or shifts in environment dynamics.
- SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation5
Experimental results indicate that the VLM-based method significantly enhances keypoint detection accuracy and task success rates, providing a more flexible and general solution for robotic garment manipulation.
- Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation4
The Hybrid Consistency Policy (HCP) is proposed, which runs a short stochastic prefix up to an adaptive switch time, and then applies a one-step consistency jump to produce the final action and yields a practical accuracy–efficiency trade-off for robot policies.
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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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