IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation

• URL: http://arxiv.org/abs/2606.10818v1
• Date: Tue, 09 Jun 2026 13:00:56 GMT
• Title: IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation
• Abstract summary: Real-world robotic manipulation tasks often involve forceful interactions with the environment.<n>We propose IMPACT, a framework that decouples these forceful tasks into task-planning and internal-model-based predictive control.
• Score: 43.12484757720005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and increase system complexity. In this work, we propose IMPACT, a framework that decouples these forceful tasks into task-planning and internal-model-based predictive control. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves higher success rates and improved generalization to unseen object weights, as well as better safety and energy efficiency.

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