Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions. To address these issues, we propose a scalable data generation pipeline that automatically augments expert demonstrations with failure recovery trajectories and fine-grained language annotations for training. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor framework, which combines failure recovery data with rich language descriptions to enhance robot control. RACER features a vision-language model (VLM) that acts as an online supervisor, providing detailed language guidance for error correction and task execution, and a language-conditioned visuomotor policy as an actor to predict the next actions. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer (RVT) on RLbench across various evaluation settings, including standard long-horizon tasks, dynamic goal-change tasks and zero-shot unseen tasks, achieving superior performance in both simulated and real world environments. Videos and code are available at: https://rich-language-failure-recovery.github.io.

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO.

Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions.

Now let's learn more about designing these two types of prompts. The Command-Xlarge model works best when we provide an instruction-based prompt. One way to do this is by using imperative verbs to tell the model what to do, for example: generate, write, list, provide, and other variations. Let's say that we are creating social media ad copy for a wireless earbuds product. We can write the prompt as follows.

Coursera Python for Everybody Specialization from University of Michigan is for those who are the complete beginners to programming language and also for who have no prior programming experience. This online coursera python for everybody course helps you to learn the basics of programming using Python Programming Language. This specialization will cover the fundamental topics of how you construct a program from a simple instruction in Python. After a general introduction to programming, coursera python for everybody teaches you how to use python to extract data from the web and work with databases. It's a good demonstration of how Python can be useful for managing large datasets.

Engineers are harnessing'swarm robotics' to teach intelligent robots how to cooperate during naval missions. The researchers in Portugal have demonstrated how a small fleet of self-learning robot boats can'think' for themselves, to work together on surveillance and other missions. Each robot is made from materials costing roughly $330, and operates with a neural network to create individual behaviours similar to those in a flock of birds. Engineers are harnessing'swarm robotics' to teach intelligent robots how to cooperate during naval missions. The researchers in Portugal have demonstrated how a small fleet of self-learning robot boats can'think' for themselves, to work together The robotic swarms work like a school of fish, or flock of birds.