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Found 10 papers, 6 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.
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial.
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.
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models
Large Language Models (LLMs) demonstrate remarkable abilities to provide human-like feedback on user inputs in natural language.
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.
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
Chat with the Environment: Interactive Multimodal Perception Using Large Language Models
However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold.
Internally Rewarded Reinforcement Learning
We study a class of reinforcement learning problems where the reward signals for policy learning are generated by an internal reward model that is dependent on and jointly optimized with the policy.
Visually Grounded Commonsense Knowledge Acquisition
In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances.
Visual Distant Supervision for Scene Graph Generation
In this work, we propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data.
