TU/e Impact-Aware Robotics Database

Welcome to the Impact-Aware Robotics Database

We present a new database supporting the development of impact-aware robotics, an emerging field of research focused on enabling robots to exploit physical impacts with objects and environments to allow for dynamic manipulation and locomotion. This database allows getting access to data that can be used to answer various research questions related to, e.g., motion planning, parameter identification, and object tracking. The database can store a wide variety of datasets obtained from recordings of impact experiments. In these experiments, robots, objects, and environments are used in the context of physical impacts to perform various robotic tasks, such as object tossing with robotic arms to speed up throughput or drones impacting walls for inspection.

Publications

Citing the Impact-Aware Robotics Database

The publication associated to the Impact-Aware Robotics Database can be found here:

The Impact-Aware Robotics Database: Supporting Research Targeting the Exploitation of Dynamic Contact Transitions

Authors:

M.J. Jongeneel, S. Dingemans, and A. Saccon

Submitted to:

IEEE Robotics and Automation Letters (RA-L)
Please use the following citation when using the Impact-Aware Robotics Database:

@ARTICLE {2023_Jongeneel,
author = {M J Jongeneel and S Dingemans and A Saccon},
title = {The Impact-Aware Robotics Database: Supporting Research Targeting the Exploitation of Dynamic Contact Transitions},
journal = {Submitted to IEEE Robotics and Automation Letters (RA-L),
year = {2023},
url = {https://hal.science/hal-03900923},
month = {TBD}}

Publications associated to datasets

The following publications use datasets that are currently stored in the Impact-Aware Robotics Database:

Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release

Authors:

M.L.S. Lubbers, J. van Voorst, M.J. Jongeneel, and A. Saccon

Published in:

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

Used dataset:

Impact Aware Manipulation (I.AM.) archive containing suction cup release experiments

Refined Post-Impact Velocity Prediction for Torque-Controlled Flexible Joint Robots

Authors:

C.A.R. Aria, W. Weekers, M. Morganti, V. Padois, and A. Saccon

Submitted to:

IEEE Robotics and Automation Letters (RA-L)

Used dataset:

I.AM. Archive containing impact experiments with a Franka Emika Panda robot

Impact-Aware Object Tracking: Exploiting Environment and Object Priors to Robustify Dynamic 6D Pose Estimation

Authors:

M.J. Jongeneel, S. Dingemans, A. Bernardino, N. van de Wouw, and A. Saccon

Submitted to:

IEEE Transactions on Robotics (T-RO)

Used dataset:

I.AM. archive containing long range box-toss experiments with Box006, Box007, and Box009 for validation of Impact-Aware Object Tracking

A Compact 6D Suction Cup Model for Robotic Manipulation via Symmetry Reduction

Authors:

A.A. Oliva, M.J. Jongeneel, and A. Saccon

Submitted to:

IEEE Transactions on Robotics (T-RO)

Used dataset:

I.AM. Archive containing experiments of a UR10 with suction gripper holding a Variable Inertia Object for suction cup stiffness identification

Evaluating the Sim-to-Real Gap for Contact-Rich Robotic Manipulation Tasks using Suction Cups

Authors:

M.J. Jongeneel, A.A. Oliva, F. Nordfeldth, R. Duarte, S. Eisinger, H. Sandee, C. Lacoursière, and A. Saccon

Submitted to:

IEEE Robotics and Automation Letters (RA-L)

Used dataset:

I.AM. Archive containing experiments of a UR10 with vacuum gripper flipping and stacking boxes in a container for sequence 1-5

Experimental Validation of Nonsmooth Dynamics Simulations for Robotic Tossing involving Friction and Impacts

Authors:

M.J. Jongeneel, L. Poort, N. van de Wouw, and A. Saccon

Submitted to:

IEEE Transactions on Robotics (T-RO)

Used dataset:

Data underlying the publication: Validating Rigid-Body Dynamics Simulators on Real-World Data for Robotic Tossing Applications