ChatGPT for Robotics: Design Principles and Model Abilities (2306.17582v2)

Abstract: This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications. We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library which allows ChatGPT to adapt to different robotics tasks, simulators, and form factors. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. We explore ChatGPT's ability to use free-form dialog, parse XML tags, and to synthesize code, in addition to the use of task-specific prompting functions and closed-loop reasoning through dialogues. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents. We show that ChatGPT can be effective at solving several of such tasks, while allowing users to interact with it primarily via natural language instructions. In addition to these studies, we introduce an open-sourced research tool called PromptCraft, which contains a platform where researchers can collaboratively upload and vote on examples of good prompting schemes for robotics applications, as well as a sample robotics simulator with ChatGPT integration, making it easier for users to get started with using ChatGPT for robotics.

• The paper presents PACT, a causal transformer model that bridges perception and action for improved real-time navigation.
• It details custom tokenization using ResNet-18 for RGB images and PointNet for LiDAR to create fixed-length embeddings.
• Experimental results reveal trade-offs between model size, sequence length, and inference speed in simulated robotic environments.

Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training

The paper "PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training" addresses the integration of perception and action in an autoregressive framework tailored for robotics applications. Specifically, the research explores the application of a causal transformer model to predict robot actions based on sensory inputs, with the intention of enhancing real-time navigation efficiency.

Experimental Methodology

The paper utilizes two distinct datasets from MuSHR and Habitat to investigate the capabilities of the proposed model. Both environments provide diverse sensory inputs—LiDAR scans in MuSHR and RGB images in Habitat—corresponding to robot actions aimed at achieving navigation goals. A significant emphasis is placed on data collection, with over 1.5 million perception-action pairs from MuSHR and 800,000 from Habitat.

Tokenizer network architectures for perceptive inputs and action commands are crafted with the intent to convert raw sensory data into fixed-length token embeddings. The paper details the specific architectures employed, such as ResNet-18 for RGB images and PointNet for LiDAR data, underscoring the customizations necessary to preserve the orientation-specific characteristics of sensory inputs.

Model Training and Architecture

The PACT model is structured around a 12-layer transformer architecture with 8 attention heads. With an embedding length of 128 and a sequence length of 16, the model is trained utilizing a rigorous set of hyperparameters aimed at balancing learning rate, weight decay, and dropout to ensure robust model performance. The paper presents a detailed account of these training parameters, as outlined in the included table, elucidating the trade-offs implicit in model design.

Empirical Findings

Upon deploying the pre-trained models in simulated environments, the research evaluates performance through metrics such as average distance traversed before a crash and action prediction accuracy. Notably, the paper reveals that while larger transformer models may perform better in terms of loss values on static datasets, real-time deployment can incur inference delays. Specifically, the largest model shows increased inference times, leading to degraded navigation performance in practical scenarios.

The attention maps, visualized for both MuSHR and Habitat environments, provide insight into the learned dynamics and token reliance across different model layers. The nuanced attention patterns reflect the ability of different heads and layers to capture temporal dependencies crucial for predicting subsequent actions.

Additional investigations into sequence length demonstrate its impact on action prediction accuracy. Longer sequences offer improved mean absolute error metrics, albeit at the cost of increased computational burden, particularly during real-time prediction tasks.

Conclusions and Future Implications

The PACT framework offers a promising approach to bridging perception and action within a unified transformer-based architecture for robotics. The paper highlights the critical balance between model size, sequence length, and real-time performance, offering guidance for future research on optimizing transformer models for embedded robotics systems.

The implications of this research are manifold, suggesting new avenues for enhancing autonomy in robotic systems through pre-training strategies and revealing the potential for causal transformers in managing complex perception-action loops. Going forward, further exploration of tokenization strategies and architectural configurations may yield more efficient and scalable models capable of adapting to various robotic applications. Additionally, expanding the approach to real-world deployments could verify the practicality and robustness of PACT in near-to-reality environments.