HIL
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Nutrient-responsive and DAF-16/FoxO target H1 histone HIL-1 promotes resistance to starvation and bacterial pathogens in Caenorhabditis elegans
Insulin/IGF-1 signaling (IIS) mediates metabolic and developmental acclimation to stressful conditions including starvation. The transcription factor DAF-16/FoxO actuates many of the physiological effects of reduced IIS, yet the specific contributions of DAF-16 target genes to stress resistance remain poorly understood. We explore the function of C. elegans H1 linker histone HIL-1/H1.0, a DAF-16 target that is upregulated during starvation.
Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation
arXiv:2606.03949v1 Announce Type: new Abstract: Human-in-the-loop reinforcement learning (HIL-RL) improves sample efficiency in real-robot manipulation through online human intervention. However, successful trajectories may include suboptimal actions that deviate from the desired task-execution path and force human intervention. Existing HIL-RL methods typically apply the consistent credit assignment principle to all transitions, uniformly propagating discounted terminal rewards through...
Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving
Announce Type: new Abstract: With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still face severe challenges in complex interaction and long-tail scenarios due to distribution shift and causal confusion. These limitations often result in a lack of human-level decision-making flexibility and safety in extreme conditions.
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
Announce Type: replace Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for grounding visual-language understanding into real-world robotic manipulation. However, dexterous manipulation remains challenging for VLA policies due to high-dimensional hand control and compounding execution errors, which makes real-world RL post-training essential for bridging the gap between visually grounded action generation and physically reliable dexterous execution. However,...