Learning with Object-centric Representations of Tactile Interactive Perception for Robot Manipulation
cs.RO
Submitted: 2026-09-27
Updated: 2026-09-27
Project page: https://xinyiyxyx.github.io/tactile-object-centric
Terminology
Sources
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Understanding Dynamic Tactile Sensing for Liquid Property Estimation
- DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions
- Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
- CLTP: Contrastive Language-Tactile Pre-training for 3D Contact Geometry Understanding
- Sparsh: Self-supervised touch representations for vision-based tactile sensing
- Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
- Self-supervised perception for tactile skin covered dexterous hands
- DINOv2: Learning Robust Visual Features without Supervision
- OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing
- TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
- ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
- PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies
- ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
- 3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
- Multi-Modal Manipulation via Multi-Modal Policy Consensus
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- A multi-modal tactile fingertip design for robotic hands to enhance dexterous manipulation
- FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
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