I own 20 axolotls - people need to know they're not easy to look after When Emma Honeyfield's daughter Amber asked for an axolotl for her birthday, Emma never imagined it would lead to a collection of 20. The 37-year-old bought her daughter's first axolotl, Stitch, in September and has since fallen in love with their calming nature. Emma said Amber, eight, had always been difficult to buy for, so when she asked for one for her birthday, she couldn't say no. And the family, from Tredegar, Blaenau Gwent, are far from alone in seeking out the amphibians, which are critically endangered and only found in lakes and wetlands in southern Mexico City . The animal's cute, smiling face and appearance in the hugely popular Minecraft and Roblox games has seen an increase in the number of people keeping them as pets.

D.2 Study 2: Additional Analysis Details Assessing a human-machine team's time-to-build, we test for normality and homoschedascity and do not reject the null hypothesis in either case, using Shapiro-Wilk (p > 0.05) and Levene's Test (p>0.7). We find a significant effect between a participant's teaming ability and the participant's build speed (F(1,26) = 23.5;p

This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textual goals to separate controllers or produce the control command directly, OmniJARVIS seeks a different path to ensure both strong reasoning and efficient decision-making capabilities via unified tokenization of multimodal interaction data. First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories $\tau = \{o_0, a_0, \dots\}$ and an imitation learning policy decoder conditioned on these tokens. These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models. With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc into unified token sequences and model them with autoregressive transformers. Thanks to the semantically meaningful behavior tokens, the resulting VLA model, OmniJARVIS, can reason (by producing chain-of-thoughts), plan, answer questions, and act (by producing behavior tokens for the imitation learning policy decoder). OmniJARVIS demonstrates excellent performances on a comprehensive collection of atomic, programmatic, and open-ended tasks in open-world Minecraft. Our analysis further unveils the crucial design principles in interaction data formation, unified tokenization, and its scaling potentials. The dataset, models, and code will be released at https://craftjarvis.org/OmniJARVIS.

Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.

We directly adopt the official default setting for Atari games. B.2 Minecraft Environment Settings Table 1 outlines how we set up and initialize the environment for each harvest task. Our method is tested in two different biomes: plains and sunflower plains. Both the plains and sunflower plains offer a wider field of view. In Minecraft, the action space is an 8-dimensional multi-discrete space.

Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem.

These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models. With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc .