2 research outputs found
Leveraging Language Models to Detect Greenwashing
In recent years, climate change repercussions have increasingly captured
public interest. Consequently, corporations are emphasizing their environmental
efforts in sustainability reports to bolster their public image. Yet, the
absence of stringent regulations in review of such reports allows potential
greenwashing. In this study, we introduce a novel methodology to train a
language model on generated labels for greenwashing risk. Our primary
contributions encompass: developing a mathematical formulation to quantify
greenwashing risk, a fine-tuned ClimateBERT model for this problem, and a
comparative analysis of results. On a test set comprising of sustainability
reports, our best model achieved an average accuracy score of 86.34% and F1
score of 0.67, demonstrating that our methods show a promising direction of
exploration for this task
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Learning Bipedal Locomotion Using Central Pattern Generators and Deep Reinforcement Learning
We present a biomechanics-based framework for the locomotion of a muscle-actuated human model that is driven by Central Pattern Generators (CPGs). Our CPG system directly generates the activation signals of 22 lower body muscles to reproduce the oscillatory patterns of locomotive bipedal stepping. We employ a dual-module architecture that trains a CPG Tuner network and a Reflex Controller to jointly adjust the muscle signals during simulation in order to adapt to the challenges of 3D bipedal locomotion. These modules are trained simultaneously using the Soft Actor-Critic (SAC) reinforcement learning algorithm. Our CPG system achieves stable, realistic walking and we observe promising results toward task-driven locomotion and adjustable gaits