# Prediction of Manipulation Actions

Source: [Prediction of Manipulation Actions](<https://www.sota2.com/research/paper/68fa62d31e209131ce9fb619>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:27:18.446Z

Authors: Cornelia Ferm\\"uller, Fang Wang, Yezhou Yang, Konstantinos Zampogiannis, Yi Zhang, Francisco Barranco, Michael Pfeiffer

Published: 2016-10-03T21:23:13.000Z

## Abstract

Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from subjects performing different manipulation actions on the same object, such as "squeezing", "flipping", "washing", "wiping" and "scratching" with a sponge. In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets showed that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithm to predict what and how a dexterous action is performed.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Action Segmentation and Recognition on 50 Salads (eval granularity)](<https://www.sota2.com/research/sota/action-segmentation-and-recognition-on-50-salads-eval-granularity.md>): Action Segmentation and Recognition; 50 Salads eval granularity; Accuracy: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1610.00759>)

[PDF](<https://arxiv.org/pdf/1610.00759.pdf>)