# Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations

arXiv: https://arxiv.org/abs/2107.00722 · HTML: https://mlsanity.com/paper/2107.00722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [End-to-End Robotic Reinforcement Learning without Reward Engineering](https://mlsanity.com/paper/1904.07854/) (2019)
- [Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation](https://mlsanity.com/paper/1810.07121/) (2018)
- [Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition](https://mlsanity.com/paper/1805.11686/) (2018)
- [Task-Embedded Control Networks for Few-Shot Imitation Learning](https://mlsanity.com/paper/1810.03237/) (2018)
- [Unsupervised Perceptual Rewards for Imitation Learning](https://mlsanity.com/paper/1612.06699/) (2016)
- [A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.01531/) (2018)
- [Few-Shot Goal Inference for Visuomotor Learning and Planning](https://mlsanity.com/paper/1810.00482/) (2018)
- [Visual Closed-Loop Control for Pouring Liquids](https://mlsanity.com/paper/1610.02610/) (2016)
- [Risk-Aware Active Inverse Reinforcement Learning](https://mlsanity.com/paper/1901.02161/) (2019)
- [Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search](https://mlsanity.com/paper/1610.00673/) (2016)
- [3D Simulation for Robot Arm Control with Deep Q-Learning](https://mlsanity.com/paper/1609.03759/) (2016)
- [Reward Learning from Narrated Demonstrations](https://mlsanity.com/paper/1804.10692/) (2018)
- [Cross-Domain Perceptual Reward Functions](https://mlsanity.com/paper/1705.09045/) (2017)

## Built on by (most cited first, 1 of 1)

- [Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination](https://mlsanity.com/paper/2208.00478/) (2022)

## Abstract (arXiv)

Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3% and 95.5% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4% and 90.3%, respectively.