Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control
Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires acting from partial observations such as noisy camera images. A common solution is teacher-student distillation, in which a visuomotor policy is trained to reproduce the actions of the privileged expert. This requires the student to jointly infer the task-relevant state and relearn the expert's action mapping that is already available. An alternative is to reuse the state-based expert and learn only a perceptual interface that reconstructs its missing state inputs. However, minimising the state estimate error alone does not necessarily minim...
| Authors | Denis Shcherba, Adrian Abel, Eckart Cobo-Briesewitz et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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