Future Success Prediction in Open-Vocabulary Object Manipulation Tasks Based on End-Effector Trajectories
This addresses the efficiency limitation in robotics by enabling pre-manipulation success prediction, though it is incremental as it builds on existing datasets and methods.
This study tackled the problem of predicting future success or failure in open-vocabulary object manipulation tasks based on end-effector trajectories, natural language instructions, and images, achieving higher prediction accuracy than baseline methods.
This study addresses a task designed to predict the future success or failure of open-vocabulary object manipulation. In this task, the model is required to make predictions based on natural language instructions, egocentric view images before manipulation, and the given end-effector trajectories. Conventional methods typically perform success prediction only after the manipulation is executed, limiting their efficiency in executing the entire task sequence. We propose a novel approach that enables the prediction of success or failure by aligning the given trajectories and images with natural language instructions. We introduce Trajectory Encoder to apply learnable weighting to the input trajectories, allowing the model to consider temporal dynamics and interactions between objects and the end effector, improving the model's ability to predict manipulation outcomes accurately. We constructed a dataset based on the RT-1 dataset, a large-scale benchmark for open-vocabulary object manipulation tasks, to evaluate our method. The experimental results show that our method achieved a higher prediction accuracy than baseline approaches.