Teaching Robots to Feel: Smarter Manipulation with Force-Aware Learning
![Across five manipulation tasks, a curriculum informed by force feedback-specifically, the CRAFT enhancement to both [latex]\pi_0[/latex]-base and RDT models-consistently elevates task success rates, with particularly pronounced improvements observed in scenarios demanding substantial physical contact, thus demonstrating the efficacy of force-aware fine-tuning.](https://arxiv.org/html/2602.12532v1/x4.png)
A new framework prioritizes tactile feedback to help robots master complex, contact-rich manipulation tasks.
![Across five manipulation tasks, a curriculum informed by force feedback-specifically, the CRAFT enhancement to both [latex]\pi_0[/latex]-base and RDT models-consistently elevates task success rates, with particularly pronounced improvements observed in scenarios demanding substantial physical contact, thus demonstrating the efficacy of force-aware fine-tuning.](https://arxiv.org/html/2602.12532v1/x4.png)
A new framework prioritizes tactile feedback to help robots master complex, contact-rich manipulation tasks.
![Agent skills are structured through a progressive disclosure architecture, loading information in stages to efficiently manage context window limitations while retaining access to complex procedural knowledge, with token estimations reflecting per-skill averages as detailed in prior work [35].](https://arxiv.org/html/2602.12430v1/x1.png)
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