Embodied Uncertainty: Prototyping Grasping Systems That Embrace and Exploit Prediction Errors

Current grasping research focuses on minimizing prediction errors and maximizing certainty. But what if uncertainty itself could be a useful tool? This research would prototype grasping systems that deliberately introduce controlled randomness and prediction errors into their control loops, inspired by how humans sometimes use "controlled sloppiness" to adapt to uncertain environments. Drawing on Namkhun et al.'s 2025 work on indirect force sensing but inverting its premise, the system would use prediction errors as signals to trigger adaptive behaviors rather than as problems to be corrected. This challenges the core assumption that robotic control should strive for perfect prediction and precision. By embracing uncertainty, the system might achieve better performance in highly variable real-world conditions where perfect prediction is impossible - essentially learning to "dance with chaos" rather than trying to eliminate it. The approach could be particularly valuable for grasping deformable, unfamiliar, or dynamically changing objects.

References:

1. Machine Learning-Based Indirect Tip Force Sensing and Estimation for Robotic Uterine Manipulation System. Songphon Namkhun, Apiwat Boonkong, P. Kaewfoongrungsi, K. Khampitak, D. Hormdee (2025). IEEE Access.