103 citations · 322 across the 32 of their papers we have counts for
7 papers · 1 filter
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
Nicklas Hansen, Yixin Lin, Hao Su +3
Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-…
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Nicklas Hansen, Zhecheng Yuan, Yanjie Ze +5
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks. We revisit a simple Learning-from-Scratch (LfS) baseline that incorporates data augmentat…
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Yifan Xu, Nicklas Hansen, Zirui Wang +3
Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so…
Visual Reinforcement Learning with Self-Supervised 3D Representations
Yanjie Ze, Nicklas Hansen, Yinbo Chen +2
A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state representation using self-supervised methods, which has the potential benefit of improved s…
Graph Inverse Reinforcement Learning from Diverse Videos
Sateesh Kumar, Jonathan Zamora, Nicklas Hansen +2
Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks. However, mo…
Temporal Difference Learning for Model Predictive Control
Nicklas Hansen, Xiaolong Wang, Hao Su
Data-driven model predictive control has two key advantages over model-free methods: a potential for improved sample efficiency through model learning, and better performance as co…
Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation
Rishabh Jangir, Nicklas Hansen, Sambaran Ghosal +2
Learning to solve precision-based manipulation tasks from visual feedback using Reinforcement Learning (RL) could drastically reduce the engineering efforts required by traditional…