Learned Action Conditioned Slip Predictor

Learned action-conditioned slip predictors aim to anticipate slippage during robotic manipulation and locomotion, improving control and stability in various applications. Current research focuses on developing data-driven models, often employing neural networks, to predict slip based on sensor data (e.g., tactile, proprioceptive) and planned actions. These models are crucial for enhancing the robustness of robots in tasks involving delicate objects or challenging terrains, leading to safer and more effective interactions with the environment. The ability to accurately predict slip enables proactive control strategies, such as adjusting grip force or gait, preventing failures and improving overall performance.