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Found 7 papers, 3 papers with code
Large Language Models for Orchestrating Bimanual Robots
Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks involving two hands is still challenging because of the difficulties in effective temporal and spatial coordination.
Causal State Distillation for Explainable Reinforcement Learning
This lack of transparency in RL models has been a long-standing problem, making it difficult for users to grasp the reasons behind an agent's behaviour.
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic
Recent advancements in large language models have showcased their remarkable generalizability across various domains.
HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network
As a result, the imaging speed is increased 16 times and the imaging lateral resolution is improved 5 times, while the deep penetration merit of AR-PAM modality is still reserved.
A Closer Look at Reward Decomposition for High-Level Robotic Explanations
Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to humans is challenging yet crucial due to their incomprehensible proprioceptive states, variational intermediate goals, and resultant unpredictability.
Reinforcement Learning (RL)
Vocal Bursts Intensity Prediction
TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval
Experimental results showed that the inference time was significantly reduced and was firstly controlled around 20ms on CPUs while at the same time the performance gain from fine-tuned BERT-Base model was mostly retained.
Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency
A novel mixture of graphical models is proposed, which dynamically couples the landmarks to a hierarchy of segments.
