# Bimanual rope manipulation skill synthesis through context dependent correction policy learning from human demonstration

arXiv: https://arxiv.org/abs/2209.13850 · HTML: https://mlsanity.com/paper/2209.13850/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor](https://mlsanity.com/paper/1910.04854/) (2019)
- [Learning Robotic Manipulation through Visual Planning and Acting](https://mlsanity.com/paper/1905.04411/) (2019)
- [Back to the Manifold: Recovering from Out-of-Distribution States](https://mlsanity.com/paper/2207.08673/) (2022)
- [Imitation and Mirror Systems in Robots through Deep Modality Blending Networks](https://mlsanity.com/paper/2106.08422/) (2021)
- [Reward Conditioned Neural Movement Primitives for Population Based Variational Policy Optimization](https://mlsanity.com/paper/2011.04282/) (2020)
- [Neural Dynamic Movement Primitives -- a survey](https://mlsanity.com/paper/2208.01903/) (2022)

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

- [Language-Guided Object-Centric Diffusion Policy for Generalizable and Collision-Aware Robotic Manipulation](https://mlsanity.com/paper/2407.00451/) (2024)

## Abstract (arXiv)

Learning from demonstration (LfD) provides a convenient means to equip robots with dexterous skills when demonstration can be obtained in robot intrinsic coordinates. However, the problem of compounding errors in long and complex skills reduces its wide deployment. Since most such complex skills are composed of smaller movements that are combined, considering the target skill as a sequence of compact motor primitives seems reasonable. Here the problem that needs to be tackled is to ensure that a motor primitive ends in a state that allows the successful execution of the subsequent primitive. In this study, we focus on this problem by proposing to learn an explicit correction policy when the expected transition state between primitives is not achieved. The correction policy is itself learned via behavior cloning by the use of a state-of-the-art movement primitive learning architecture, Conditional Neural Motor Primitives (CNMPs). The learned correction policy is then able to produce diverse movement trajectories in a context dependent way. The advantage of the proposed system over learning the complete task as a single action is shown with a table-top setup in simulation, where an object has to be pushed through a corridor in two steps. Then, the applicability of the proposed method to bi-manual knotting in the real world is shown by equipping an upper-body humanoid robot with the skill of making knots over a bar in 3D space. The experiments show that the robot can perform successful knotting even when the faced correction cases are not part of the human demonstration set.