Robust robotic manipulation under degraded sensing requires combining hardware redundancy, model-based control, perception alternatives, and adaptive learning so systems remain precise when primary sensors degrade. Causes of degradation include environmental factors such as dust, low light, moisture, or temperature extremes; mechanical wear and cable faults; and occlusions during close manipulation. Consequences range from reduced task accuracy and increased cycle time in factories to safety risks in healthcare or field robotics, and mission loss in space or undersea operations. Human and cultural contexts matter: industrial settings can standardize redundancy more easily than low-cost consumer robots in homes, and territorial challenges like Arctic deployments intensify sensor failure modes.
Redundancy and sensor fusion
Maintaining precision often starts with redundancy and sensor fusion that weight remaining signals when one modality falters. Davide Scaramuzza University of Zurich and ETH Zurich has advanced visual-inertial approaches and advocated alternate sensors such as event cameras that preserve information under motion blur and low light, improving pose estimation when conventional cameras fail. Robert D. Howe Harvard University has emphasized integrating tactile and force sensing to complement vision; tactile inputs provide direct contact information that remains available during visual occlusion. Combining proprioceptive joint encoders, force/torque sensors, and multiple camera viewpoints reduces single-point failure impact and supports consistent end-effector positioning.
Control, adaptation, and learning
When sensing quality changes, controllers must compensate through robust and adaptive control. Naira Hovakimyan University of Illinois Urbana-Champaign has developed adaptive control frameworks that maintain stability and tracking despite unmodeled disturbances and degraded measurements, enabling continued precise manipulation. Learning-based strategies add resilience by training policies that tolerate noisy or missing inputs; Sergey Levine University of California Berkeley and Pieter Abbeel University of California Berkeley have shown that end-to-end learning and domain randomization produce policies that generalize across sensor variations, while reinforcement learning complemented by model-based estimation recovers performance even when parts of the sensor suite intermittently drop out. Ken Goldberg University of California Berkeley has studied manipulation under uncertainty and shown that uncertainty-aware planning can trade speed for reliability to preserve task success.
Practical deployment blends these elements: hardware choices that suit the operating environment, diagnostic layers that detect sensor loss and reweight estimators, controllers that adapt gains or switch modes, and learned components that fill perceptual gaps. The interplay affects stakeholders differently: manufacturers can invest heavily in redundancy, while consumer devices may rely more on software adaptation. Environmental considerations such as salt corrosion or extreme cold demand specialized sensors and maintenance regimes. Together, these strategies help robots sustain precise manipulation when sensors degrade, protecting performance, safety, and mission outcomes.