6 citations · 17 across the 15 of their papers we have counts for
7 papers · 1 filter
FINE: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation
Khang H. Nguyen, Hoang Pham Quang Nguyen, Ha Phuong Nguyen +6
Adapting vision-language navigation (VLN) policies to new environments is expensive because every additional route and instruction requires an embodied demonstration. Yet standard…
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…
TD-GRPC: Temporal Difference Learning with Group Relative Policy Constraint for Humanoid Locomotion
Khang Nguyen, Khai Nguyen, An T. Le +4
Robot learning in high-dimensional control settings, such as humanoid locomotion, presents persistent challenges for reinforcement learning (RL) algorithms due to unstable dynamics…
Enhancing Exploration with Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation
Huy Le, Tai Hoang, Miroslav Gabriel +2
Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance ex…
Multi-Arm Bin-Picking in Real-Time: A Combined Task and Motion Planning Approach
Ilyes Toumi, Andreas Orthey, Alexander von Rohr +1
Automated bin-picking is a prerequisite for fully automated manufacturing and warehouses. To successfully pick an item from an unstructured bin the robot needs to first detect poss…
Differentiable Robust LQR Layers
Ngo Anh Vien, Gerhard Neumann
This paper proposes a differentiable robust LQR layer for reinforcement learning and imitation learning under model uncertainty and stochastic dynamics. The robust LQR layer can ex…
Bayes-Adaptive Deep Model-Based Policy Optimisation
Tai Hoang, Ngo Anh Vien
We introduce a Bayesian (deep) model-based reinforcement learning method (RoMBRL) that can capture model uncertainty to achieve sample-efficient policy optimisation. We propose to…