IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation
Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by low-level impedance controllers. In these systems, forceful interactions are either implicitly realized through steady-state tracking errors or explicitly commanded using wrist force/torque or tactile sensors. However, implicit approaches generalize poorly across object weights, while explicit approaches require specialized hardware ...
| Authors | Jiawei Gao, Chaoqi Liu, Peilin Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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