Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration
cs.RO
Submitted: 2026-10-13
Updated: 2026-10-13
Project page: https://ctp-robotics.github.io/project_page
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- RoboTTT: Context Scaling for Robot Policies
- ICLR: In-Context Imitation Learning with Visual Reasoning
- Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers
- What Enables In-Context Behavior Prompting for Manipulation?
- Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
- Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation
- OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
- RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
- Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
- Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
- Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
- Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation
- AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
- $N_0$-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation
- TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
- VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
- FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
- Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
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