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Found 9 papers, 1 papers with code
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
On the other side are perhaps over-pessimistic claims that all that LLMs are good for in planning/reasoning tasks are as mere translators of the problem specification from one syntactic format to another, and ship the problem off to external symbolic solvers.
Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?
In this work, we explore the task of Perceived Behavior Recognition, where a robot employs a Large Language Model (LLM) to assess the robot's generated behavior in a manner similar to human observer.
Benchmarking Multi-Agent Preference-based Reinforcement Learning for Human-AI Teaming
We introduce the notion of Human-Flexibility, i. e. whether the human partner is amenable to multiple team strategies, with a special case being Specified Orchestration where the human has a single team policy in mind (most constrained case).
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning
Preference Based Reinforcement Learning has shown much promise for utilizing human binary feedback on queried trajectory pairs to recover the underlying reward model of the Human in the Loop (HiL).
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach
In this paper we address the solution of the popular Wordle puzzle, using new reinforcement learning methods, which apply more generally to adaptive control of dynamic systems and to classes of Partially Observable Markov Decision Process (POMDP) problems.
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping
In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks.
Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments
We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches.
Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop
Rapid advancements in the Internet of Things (IoT) have facilitated more efficient deployment of smart environment solutions for specific user requirement.
A Survey of Black-Box Adversarial Attacks on Computer Vision Models
Machine learning has seen tremendous advances in the past few years, which has lead to deep learning models being deployed in varied applications of day-to-day life.
