2 citations · 7 across the 13 of their papers we have counts for
17 papers
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…
Diminishing Return of Value Expansion Methods
Daniel Palenicek, Michael Lutter, João Carvalho +3
Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. T…
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…
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
Daniel Palenicek, Michael Lutter, Joao Carvalho +1
Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled traj…
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…
An Analysis of Measure-Valued Derivatives for Policy Gradients
Joao Carvalho, Jan Peters
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate…
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients
João Carvalho, Davide Tateo, Fabio Muratore +1
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate…
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, João Carvalho, Jan Peters
Off-policy Reinforcement Learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current…
A Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, Joao Carvalho, Hany Abdulsamad +1
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is es…