4 papers
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization
Tianci Gao, Konstantin A. Neusypin, Dmitry D. Dmitriev +2
Proximal Policy Optimization (PPO) is widely used in continuous control due to its robustness and stable training, yet it remains sample-inefficient in tasks with expensive interac…
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors
Ruoxuan Feng, Jiangyu Hu, Wenke Xia +5
Visuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous meticulously designed visuo-tacti…
Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration
Gao Tianci
This paper presents an innovative application of Transformer-XL for long sequence tasks in robotic learning from demonstrations (LfD). The proposed framework effectively integrates…
Enhancing Robotic Adaptability: Integrating Unsupervised Trajectory Segmentation and Conditional ProMPs for Dynamic Learning Environments
Tianci Gao
We propose a novel framework for enhancing robotic adaptability and learning efficiency, which integrates unsupervised trajectory segmentation with adaptive probabilistic movement…