# Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations

arXiv: https://arxiv.org/abs/2104.13907 · HTML: https://mlsanity.com/paper/2104.13907/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://mlsanity.com/paper/1704.06888/) (2017)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1810.10191/) (2018)
- [Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation](https://mlsanity.com/paper/1806.08756/) (2018)
- [Self-Supervised Correspondence in Visuomotor Policy Learning](https://mlsanity.com/paper/1909.06933/) (2019)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [Learning Actionable Representations from Visual Observations](https://mlsanity.com/paper/1808.00928/) (2018)
- [A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes](https://mlsanity.com/paper/1910.00127/) (2019)
- [Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning](https://mlsanity.com/paper/2003.02637/) (2020)
- [Fighting Failures with FIRE: Failure Identification to Reduce Expert Burden in Intervention-Based Learning](https://mlsanity.com/paper/2007.00245/) (2020)
- [Learning Robust Bed Making using Deep Imitation Learning with DART](https://mlsanity.com/paper/1711.02525/) (2017)
- [Visual Task Progress Estimation with Appearance Invariant Embeddings for Robot Control and Planning](https://mlsanity.com/paper/2003.06977/) (2020)

## Built on by (most cited first, 5 of 5)

- [Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks](https://mlsanity.com/paper/2301.00051/) (2022)
- [Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor](https://mlsanity.com/paper/2311.01248/) (2023)
- [On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting](https://mlsanity.com/paper/2504.13618/) (2025)
- [Object-Centric Mobile Manipulation through SAM2-Guided Perception and Imitation Learning](https://mlsanity.com/paper/2507.10899/) (2025)
- [Working Backwards: Learning to Place by Picking](https://mlsanity.com/paper/2312.02352/) (2023)

## Abstract (arXiv)

Learned visuomotor policies have shown considerable success as an alternative to traditional, hand-crafted frameworks for robotic manipulation. Surprisingly, an extension of these methods to the multiview domain is relatively unexplored. A successful multiview policy could be deployed on a mobile manipulation platform, allowing the robot to complete a task regardless of its view of the scene. In this work, we demonstrate that a multiview policy can be found through imitation learning by collecting data from a variety of viewpoints. We illustrate the general applicability of the method by learning to complete several challenging multi-stage and contact-rich tasks, from numerous viewpoints, both in a simulated environment and on a real mobile manipulation platform. Furthermore, we analyze our policies to determine the benefits of learning from multiview data compared to learning with data collected from a fixed perspective. We show that learning from multiview data results in little, if any, penalty to performance for a fixed-view task compared to learning with an equivalent amount of fixed-view data. Finally, we examine the visual features learned by the multiview and fixed-view policies. Our results indicate that multiview policies implicitly learn to identify spatially correlated features.