A few videos issued from various research projects.
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
On this Page
A few videos issued from various research projects.
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
These videos are also shown within research projects, along with longer discussions of the research they illustrate.
Robot Manipulation of Amorphous Materials (2025)
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Uncertainty-aware RL with VAE State Denoising for High-occlusion Tasks (2025)
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Distributing sand homogeneously on a target surface, despite major visual occlusions incurred by the sand plume. More information. Please cite (Du et al., 2025).
Diffusion-based Grasp Planning with Local Geometric Features
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
There are two dominant approaches in modern robot grasp planning: dense prediction and sampling-based methods. Dense prediction calculates viable grasps across the robot’s view but is limited to predicting one grasp per voxel. Sampling-based methods, on the other hand, encode multi-modal grasp distributions, allowing for different grasp approaches at a point. However, these methods rely on a global latent representation, which struggles to represent the entire field of view, resulting in coarse grasps. To address this, we introduce Implicit Grasp Diffusion (IGD), which combines the strengths of both methods by using implicit neural representations to extract detailed local features and sampling grasps from diffusion models conditioned on these features. More information. Please cite (Song et al., 2024).
Multimodal Hand Trajectory Anticipation and Shared Control (2024)
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
We present an intent estimator, dubbed the Robot Trajectron (RT), that produces a probabilistic representation of the robot’s anticipated trajectory based on its recent position, velocity and acceleration history. By taking arm dynamics into account, RT can capture the operator’s intent better than other SOTA models that only use the arm’s position, making it particularly well-suited to assist in tasks where the operator’s intent is susceptible to change. We derive a novel shared-control solution that combines RT’s predictive capacity to a representation of the locations of potential reaching targets. More information. Please cite (Song et al., 2024).
Autonomous Climbing (2019)
Beautiful video of a field test we ran in Death Valley in December 2018:
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Learning prototypical parts by which objects are often grasped, to the end of grasping novel objects. Please cite (Detry et al., 2013).
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Video illustrating the capabilities of a robot that learned how to grasp via the method illustrated in the video directly below.
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Video illustrating the part learning process.
Grasping Using Tactile And Visual Data (2015)
The robot learns what it feels like to grasp an object. This way, it can abort a grasp that feels unstable before lifting up (and potentially breaking) the object. Please cite (Bekiroglu et al., 2011; Hyttinen et al., 2015)
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Video illustrating pose- and touch-based grasp stability estimation.
If first html tag is indented, and this include is called after a list, the html tag is considered a list element, and things go wrong. Leaving a hidden unindented line here
Video illustrating pose- and touch-based grasp stability estimation.
References
AREPO: Uncertainty-Aware Robot Ensemble Learning Under Extreme Partial Observability.
Yurui
Du, Louis
Hanut, Herman
Bruyninckx, and Renaud
Detry.
@article{du2025a,author={Du, Yurui and Hanut, Louis and Bruyninckx, Herman and Detry, Renaud},title={AREPO: Uncertainty-Aware Robot Ensemble Learning Under Extreme Partial Observability},year={2025},doi={10.1109/LRA.2025.3554451},journal={{IEEE} Robotics and Automation Letters},}
Robotic Framework for Iterative and Adaptive Profile Grading of Sand.
Louis
Hanut, Yurui
Du, Andrew
Vande Moere, Renaud
Detry, and Herman
Bruyninckx.
In IEEE International Conference on Robotics and Automation, 2025.
@inproceedings{hanut2025a,author={Hanut, Louis and Du, Yurui and Vande Moere, Andrew and Detry, Renaud and Bruyninckx, Herman},title={Robotic Framework for Iterative and Adaptive Profile Grading of Sand},year={2025},booktitle={{IEEE} International Conference on Robotics and Automation},}
Implicit Grasp Diffusion: Bridging the Gap between Dense Prediction and Sampling-based Grasping.
@inproceedings{song2024b,author={Song, Pinhao and Li, Pengteng and Detry, Renaud},title={Implicit Grasp Diffusion: Bridging the Gap between Dense Prediction and Sampling-based Grasping},year={2024},booktitle={Conference on Robot Learning},}
Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation.
Pinhao
Song, Pengteng
Li, Erwin
Aertbelien, and Renaud
Detry.
In IEEE International Conference on Robotics and Automation, 2024.
@inproceedings{song2024a,author={Song, Pinhao and Li, Pengteng and Aertbelien, Erwin and Detry, Renaud},title={Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation},year={2024},booktitle={{IEEE} International Conference on Robotics and Automation},doi={10.1109/ICRA57147.2024.10611507},}
Learning the Tactile Signatures of Prototypical Object Parts for Robust Part-based Grasping of Novel Objects.
Emil
Hyttinen, Danica
Kragic, and Renaud
Detry.
In IEEE International Conference on Robotics and Automation, 2015.
@inproceedings{hyttinen2015a,author={Hyttinen, Emil and Kragic, Danica and Detry, Renaud},title={Learning the Tactile Signatures of Prototypical Object Parts for Robust Part-based Grasping of Novel Objects},year={2015},booktitle={{IEEE} International Conference on Robotics and Automation},doi={https://dx.doi.org/10.1109/ICRA.2015.7139883},}
Learning a Dictionary of Prototypical Grasp-predicting Parts from Grasping Experience.
Renaud
Detry, Carl Henrik
Ek, Marianna
Madry, and Danica
Kragic.
In IEEE International Conference on Robotics and Automation, 2013.
@inproceedings{detry2013a,author={Detry, Renaud and Ek, Carl Henrik and Madry, Marianna and Kragic, Danica},title={Learning a Dictionary of Prototypical Grasp-predicting Parts from Grasping Experience},year={2013},booktitle={{IEEE} International Conference on Robotics and Automation},doi={10.1109/ICRA.2013.6630635},}
Learning Grasp Affordance Densities.
R.
Detry, D.
Kraft, O.
Kroemer, L.
Bodenhagen, J.
Peters, N.
Krüger, and J.
Piater.
@article{detry2011a,author={Detry, R. and Kraft, D. and Kroemer, O. and Bodenhagen, L. and Peters, J. and Krüger, N. and Piater, J.},title={Learning Grasp Affordance Densities},year={2011},doi={10.2478/s13230-011-0012-x},journal={Paladyn.\ Journal of Behavioral Robotics},number={1},pages={1--17},volume={2},}
Learning Tactile Characterizations Of Object- And Pose-specific Grasps.
Yasemin
Bekiroglu, Renaud
Detry, and Danica
Kragic.
In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011.
@inproceedings{bekiroglu2011d,author={Bekiroglu, Yasemin and Detry, Renaud and Kragic, Danica},title={Learning Tactile Characterizations Of Object- And Pose-specific Grasps},year={2011},booktitle={{IEEE/RSJ} International Conference on Intelligent Robots and Systems},doi={10.1109/IROS.2011.6094878},}
DOI
Bib
PDF
