Interaction Latents as Actions for Contact-Rich Manipulation
cs.RO
Submitted: 2026-10-13
Updated: 2026-10-14
Project page: https://mrsecant.github.io/ILA
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
- ForceDelta-VLA: Distilling Force-Conditioned ActionCorrections for Contact-Rich Manipulation
- AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors
- AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception
- HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing
- Sparsh: Self-supervised touch representations for vision-based tactile sensing
- 3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
- Diffusion Reward: Learning Rewards via Conditional Video Diffusion
- TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation
- TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction
- Offline Reinforcement Learning with Implicit Q-Learning
- Behavior Generation with Latent Actions
- Flow Matching for Generative Modeling
- Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
- VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
- AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
- $N_0$-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation
- T-Rex: Tactile-Reactive Dexterous Manipulation
- DINOv2: Learning Robust Visual Features without Supervision
- Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
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