2 citations · 7 across the 13 of their papers we have counts for
11 papers · 1 filter
Do My Samples Help? Compute Budget Allocation for Real-World Sampling-Based MPC in Contact-Rich Manipulation
Magnus Dierking, João Carvalho, Vince Kurtz +3
Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-time evaluation of large num…
Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations
Chunyu Jiang, Yichen Cai, Shuai Li +6
Contact-rich manipulation requires a robot to be precise in free space to establish the intended contacts, and compliant once in contact to avoid excessive forces. Imitation learni…
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
Magnus Dierking, Joao Carvalho, An Thai Le +2
Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with massively parallel GPU simul…
Model Tensor Planning
An T. Le, Khai Nguyen, Minh Nhat Vu +2
Sampling-based model predictive control (MPC) offers strong performance in nonlinear and contact-rich robotic tasks, yet often suffers from poor exploration due to locally greedy s…
Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3
Joao Carvalho, An T. Le, Philipp Jahr +4
Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large dat…
Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models
J. Carvalho, A. Le, P. Kicki +2
The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain…
Global Tensor Motion Planning
An T. Le, Kay Hansel, João Carvalho +5
Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This…
ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching
Niklas Funk, Julen Urain, Joao Carvalho +3
Spatial understanding is a critical aspect of most robotic tasks, particularly when generalization is important. Despite the impressive results of deep generative models in complex…
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models
Joao Carvalho, An T. Le, Mark Baierl +2
Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as prio…
A Hierarchical Approach to Active Pose Estimation
Jascha Hellwig, Mark Baierl, Joao Carvalho +2
Creating mobile robots which are able to find and manipulate objects in large environments is an active topic of research. These robots not only need to be capable of searching for…
Residual Robot Learning for Object-Centric Probabilistic Movement Primitives
Joao Carvalho, Dorothea Koert, Marek Daniv +1
It is desirable for future robots to quickly learn new tasks and adapt learned skills to constantly changing environments. To this end, Probabilistic Movement Primitives (ProMPs) h…