23 citations · 27 across the 7 of their papers we have counts for
8 papers
ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning
Tuan Van Vo, Tan Q. Nguyen, Khang Nguyen +5
Vision-Language-Action (VLA) models have gained much attention from the research community thanks to their strength in translating multimodal observations with linguistic instructi…
Accelerating Motion Planning via Optimal Transport
An T. Le, Georgia Chalvatzaki, Armin Biess +1
Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder real-time, efficie…
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…
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning
Georgia Chalvatzaki, Ali Younes, Daljeet Nandha +3
Long-horizon task planning is essential for the development of intelligent assistive and service robots. In this work, we investigate the applicability of a smaller class of large…
Hierarchical Policy Blending As Optimal Transport
An T. Le, Kay Hansel, Jan Peters +1
We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT hierarchically adjusts the weights of low-level reactive expert policies of different agents by adding…
Learning Implicit Priors for Motion Optimization
Julen Urain, An T. Le, Alexander Lambert +3
In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization. EBMs are a set of neural networks that can represent expr…
Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
An T. Le, Meng Guo, Niels van Duijkeren +4
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes an…
Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks
An T. Le, Philipp Kratzer, Simon Hagenmayer +2
In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Moti…