# Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search

arXiv: https://arxiv.org/abs/2205.08189 · HTML: https://mlsanity.com/paper/2205.08189/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 22 of 22)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations](https://mlsanity.com/paper/1912.04344/) (2019)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://mlsanity.com/paper/1803.11469/) (2018)
- [Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning](https://mlsanity.com/paper/1708.02190/) (2017)
- [Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation](https://mlsanity.com/paper/1810.07121/) (2018)
- [Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm](https://mlsanity.com/paper/1610.05729/) (2016)
- [Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors](https://mlsanity.com/paper/1905.11874/) (2019)
- [Unsupervised Learning and Exploration of Reachable Outcome Space](https://mlsanity.com/paper/1909.05508/) (2019)
- [Model-Based Quality-Diversity Search for Efficient Robot Learning](https://mlsanity.com/paper/2008.04589/) (2020)
- [From exploration to control: learning object manipulation skills through novelty search and local adaptation](https://mlsanity.com/paper/1901.00811/) (2019)
- [Novelty Search makes Evolvability Inevitable](https://mlsanity.com/paper/2005.06224/) (2020)
- [Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms](https://mlsanity.com/paper/2105.00682/) (2021)
- [qiBullet, a Bullet-based simulator for the Pepper and NAO robots](https://mlsanity.com/paper/1909.00779/) (2019)

## Built on by (most cited first, 9 of 9)

- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [Evolutionary Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.04150/) (2023)
- [Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning](https://mlsanity.com/paper/2305.13795/) (2023)
- [CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories](https://mlsanity.com/paper/2403.13208/) (2024)
- [Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2210.02622/) (2022)
- [Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets](https://mlsanity.com/paper/2310.04517/) (2023)
- [Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics](https://mlsanity.com/paper/2308.05483/) (2023)
- [E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories](https://mlsanity.com/paper/2210.07887/) (2022)
- [Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search](https://mlsanity.com/paper/2210.11801/) (2022)

## Abstract (arXiv)

Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the object. Learning methods could help to autonomously discover relevant grasping movements, but they face an important issue: grasping movements are so rare that a learning method based on exploration has little chance to ever observe an interesting movement, thus creating a bootstrap issue. We introduce an approach to generate diverse grasping movements in order to solve this problem. The movements are generated in simulation, for particular object positions. We test it on several simulated robots: Baxter, Pepper and a Kuka Iiwa arm. Although we show that generated movements actually work on a real Baxter robot, the aim is to use this method to create a large dataset to bootstrap deep learning methods.