Danny Driess

Doctoral researcher
mail: danny.driess@campus.tu-berlin.de
 

Danny Driess is a doctoral researcher in Marc Toussaint’s team. His PhD focuses on learning in Task and Motion Planning with a special focus on connecting perception and planning for sequential manipulation tasks through machine learning.
Within SCIoI, he investigates novel ways to couple sequential manipulation planning with reactive policies for execution. He received his Bachelor of Science (with distinction) in Simulation Technology in 2016 and his Master of Science (with distinction) in Simulation Technology in 2019, both from the University of Stuttgart.
At SCIoI, he is currently working on Project 39.

SCIoI Publications

Driess, D., Schubert, I., Florence, P., Li, Y., & Toussaint, and M. (2022). Reinforcement Learning with Neural Radiance Fields. NeurIPS 2022. https://dannydriess.github.io/nerf-rl
Driess, D., Huang, Z., Li, Y., Tedrake, R., & Toussaint, M. (2022). Learning Multi-Object Dynamics with Compositional Neural Radiance Fields. CoRL 2022. https://dannydriess.github.io/compnerfdyn/
Ortiz-Haro, J., Ha, J.-S., Driess, D., & Toussaint, M. (2022). Structured deep generative models for sampling on constraint manifolds in sequential manipulation. CoRL 2021. https://argmin.lis.tu-berlin.de/papers/21-ortiz-CORL.pdf
Ha, J.-S., Driess, D., & Toussaint, M. (2022). Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input. IEEE Robotics and Automation Letters. https://arxiv.org/pdf/2112.04812
Toussaint, M., Harris, J., Ha, J.-S., Driess, D., & Hönig, W. (2022). Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation. IROS 2022. https://www.user.tu-berlin.de/mtoussai/22-SecMPC/
Harris, J., Driess, D., & Toussaint, M. (2022). FC3: Feasibility-Based Control Chain Coordination. IROS 2022. https://arxiv.org/pdf/2205.04362
Toussaint, M., Harris, J., Ha, J.-S., Driess, D., & Hönig, W. (2022). Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation. arxiv.:2203.05390. https://www.user.tu-berlin.de/mtoussai/22-SecMPC/
Schubert, I., Driess, D., Oguz, O. S., & Toussaint, M. (2021). Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. NeurIPS 2021. https://argmin.lis.tu-berlin.de/papers/21-schubert-NeurIPS.pdf
Driess, D., Ha, J.-S., Toussaint, M., & Tedrake, R. (2021). Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning. CoRL 2021. http://arxiv.org/abs/2110.00792
Driess, D., Ha, J.-S., & Toussaint, M. (2021). Learning to solve sequential physical reasoning problems from a scene image. The International Journal of Robotics Research, 40(12–14), 1435–1466. https://doi.org/10.1177/02783649211056967