11 accepted papers
In this paper, we introduce Haptic-Informed ACT, an advanced robotic system for pseudo oocyte manipulation, integrating multimodal information and Action Chunking with Transformers (ACT). Traditional automation methods for oocyte transfer rely heavily on visual perception, often requiring human supe
Effectively modeling the spatio-temporal interactions both internally and externally is a challenge in controlling multi-linked snake robots. This paper presents an effective method based on deep predictive coding: SnakeFormer, to address the aforementioned issue. The main contributions include: 1)
In this paper, we introduce a double-stage transfer learning framework based on expert data. It employs probabilistic graphical models to effectively capture helical periodic features in the latent space, integrating Bayesian variational inference and neural networks for implementation. Compared to…
This paper addresses the lack of research on periodic reinforcement learning for physical robot control by presenting a 3-phase periodic Bayesian reinforcement learning method for uncertain environments. Drawing on cognition theory, the proposed approach achieves effective convergence with fewer tra…
The majority of current research on reinforcement learning (RL) for snake robot control do not sufficiently account for the spatial and temporal dependencies within the robot or its interaction with its environment during movement. To address this issue, we propose an RL based multi-layer Bayesian m…
High-resolution (HR) magnetic resonance (MR) images could provide reliable visual information for clinical diagnosis. Recently, super-resolution (SR) methods based on convolutional neural networks (CNNs) have shown great potential in obtaining HR MR images. However, most existing CNN-based SR method…
Reinforcement learning commonly suffers from slow convergence speed and requires thousands of episodes, which makes it hard to be applied for physical robotic applications. Little research has been studied for snake robot control using RL because of the additional difficulty of high redundancy of fr…
Reinforcement Learning (RL) usually needs thousands of episodes, leading its applications on physical robots expensive and challenging. Little research has been reported about snake robot control using RL due to additional difficulty of high redundancy of freedom. We propose a coach-based deep learn
Snake robot control usually adopts a centralized framework using high dimensional states, which suffers from exponentially increased computational cost with module number. Although decentralized control schemes have been proposed by using central pattern generators, the dynamical correlation among s
This paper presents a novel Bayesian-based controller for snake robots in cluttered environment. It extends the conventional shape-based compliant control into statistical field providing an explicit mathematical formulation with Bayesian network. A sequential density propagation rule is derived by…
We propose a novel model-based network to reconstruct the magnetic resonance (MR) image. In this network, the Approximate Message Passing (AMP) algorithm is unrolled to solve the optimization problem of compressed sensing MR imaging, and several CNN blocks is embedded as de-aliasing steps. We relax…