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Future Success Prediction in Open-Vocabulary Object Manipulation Tasks Based on End-Effector Trajectories

Motonari Kambara, Komei Sugiura

TL;DR

The paper tackles predicting future success of open-vocabulary object manipulations using a pre-manipulation egocentric image, an end-effector trajectory, and a natural language instruction. It introduces two key components—the Trajectory Encoder and the $\lambda$-Representation Encoder—to align temporal trajectory dynamics and language-aligned visual features, enabling prediction before manipulation via the probability $p(\hat{y}=1)$. Evaluated on an SP-RT-1/RT-1-derived dataset with 13,915 samples, the method achieves $83.4\%$ accuracy, outperforming a strong baseline by $8.5$ percentage points, with ablations showing modest gains when removing the Trajectory Encoder. The results demonstrate the practical benefit of pre-execution success prediction for efficient and safer open-vocabulary manipulation, and point to future work on richer trajectory utilization as visual prompts.

Abstract

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.

Future Success Prediction in Open-Vocabulary Object Manipulation Tasks Based on End-Effector Trajectories

TL;DR

The paper tackles predicting future success of open-vocabulary object manipulations using a pre-manipulation egocentric image, an end-effector trajectory, and a natural language instruction. It introduces two key components—the Trajectory Encoder and the -Representation Encoder—to align temporal trajectory dynamics and language-aligned visual features, enabling prediction before manipulation via the probability . Evaluated on an SP-RT-1/RT-1-derived dataset with 13,915 samples, the method achieves accuracy, outperforming a strong baseline by percentage points, with ablations showing modest gains when removing the Trajectory Encoder. The results demonstrate the practical benefit of pre-execution success prediction for efficient and safer open-vocabulary manipulation, and point to future work on richer trajectory utilization as visual prompts.

Abstract

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.
Paper Structure (12 sections, 3 figures, 1 table)

This paper contains 12 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Statement
  4. Proposed Method
  5. Trajectory Encoder
  6. $\lambda$-Representation Encoder
  7. Experiments
  8. Experimental Settings
  9. Quantitative Results
  10. Qualitative Results
  11. Ablation Study
  12. Conclusions

Figures (3)

  • Figure 1: Task Example. "Pick an apple from the white bowl." is provided as a natural language instruction. In this example, the model is expected to predict 'Success' because the object manipulation is performed appropriately.
  • Figure 2: Network overview of the proposed method. 'Conv' and 'MLP' represent the convolutional layer and multi-layer perceptron, respectively.
  • Figure 3: Qualitative results. Panels (i), (ii), and (iii) represent the True Positive, True Negative, and False Negative examples, respectively. The leftmost image in each panel illustrates the scene before manipulation.