SOURCE-LINKED INTELLIGENCE
TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback
Contact-rich manipulation requires adapting to contact states that can evolve substantially within an action horizon. However, chunk-based vision-language-action models predict complete action chunks from observations collected before execution, leaving tactile conditioning stale during execution. Existing tactile-reactive approaches typically rely on separate high-frequency controllers, which increase both architectural and training complexity. In this paper, we introduce TacForcing, a streaming action-generation framework that effectively incorporates execution-time tactile feedback. Instead
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T13:48:52.000Z
First collected: 2026-09-21T09:22:01.459Z. This is not the publication date.