In recent years, the integration of large language models (LLMs) has revolutionized the field
of robotics, enabling robots to communicate, understand, and reason with human-like …

Humanoid robots, with their human-like embodiment, have the potential to integrate
seamlessly into human environments. Critical to their coexistence and cooperation with …

We introduce a new benchmark, LLF-Bench (Learning from Language Feedback
Benchmark; pronounced as" elf-bench"), to evaluate the ability of AI agents to interactively …

Autoregressive models have demonstrated remarkable success in natural language
processing. In this work, we design a simple yet effective autoregressive architecture for …

Large-scale generative language and vision-language models (LLMs and VLMs) excel in
few-shot in-context learning for decision making and instruction following. However, they …

得益于近期具有世界知识的大规模预训练模型的迅速发展, 基于大模型的具身智能在各类任务中
取得了良好的效果, 展现出了强大的泛化能力与在各领域内广阔的应用前景. 鉴于此 …

Large-scale generative language and vision-language models excel in in-context learning
for decision making. However, they require high-quality exemplar demonstrations to be …

Learning abstract state representations and knowledge is crucial for long-horizon robot
planning. We present InterPreT, an LLM-powered framework for robots to learn symbolic …

This paper explores how non-experts can teach robots desired skills in their environments.
We argue that natural language is an intuitive and accessible interface for robot learning. To …

Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-
tune or train agentic machine learning models. Similar to how humans interact in social …