VIMA: General Robot Manipulation with Multimodal Prompts (2210.03094v2)

Abstract: Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose LLM can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to $2.9\times$ task success rate given the same training data. With $10\times$ less training data, VIMA still performs $2.7\times$ better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/

• The paper introduces a novel framework that unifies diverse robotic manipulation tasks with interleaved textual and visual inputs.
• The VIMA-Bench benchmark and transformer architecture deliver up to 2.9 times higher task success in challenging zero-shot settings.
• The research demonstrates efficient imitation learning and data scalability, paving the way for more adaptable, generalist robotic systems.

An Overview of VIMA: General Robot Manipulation with Multimodal Prompts

The paper "VIMA: General Robot Manipulation with Multimodal Prompts" proposes a novel framework for enhancing robotic manipulation by leveraging multimodal prompts. The research introduces VIMA, a transformer-based robotic agent designed to process and understand interleaved textual and visual inputs to execute a wide range of manipulation tasks. This approach aims to unify task specification for robotics under a single prompt-driven model akin to recent advancements in NLP, available through LLMs.

Key Contributions

1. Multimodal Prompting Formulation: The authors propose a novel representation where various robot manipulation tasks can be encapsulated using sequences that include both text and images. This allows the conversion of tasks into a unified sequence modeling problem, facilitating a smoother integration of complex, diverse task specifications into a single coherent framework.
2. VIMA-Bench Simulation Benchmark: A new benchmark, VIMA-Bench, is introduced to evaluate the capabilities of robotic agents using multimodal prompts. Built on the Ravens simulator, it supports a variety of tasks with multimodal prompt templates and includes a comprehensive dataset of expert trajectories for imitation learning.
3. Transformer-Based Robot Agent (VIMA): The paper presents VIMA, a robot agent that employs a scalable transformer architecture for multi-task learning. The model is trained using imitation learning from a large dataset of multimodal tasks and auto-regressively generates motor actions based on prompt and interaction history.

Numerical Results and Claims

VIMA demonstrates substantial success in zero-shot generalization. The experimental results indicate that VIMA achieves a task success rate of up to 2.9 times higher than alternative architectures in the most challenging generalization settings, using the same volume of training data. Moreover, even with a tenth of the training data, VIMA performs 2.7 times better compared to the best competing models. This highlights the efficiency and scalability of the proposed architecture over traditional end-to-end learning from raw inputs.

Evaluation Protocol

The benchmark employs a four-level evaluation protocol to systematically assess zero-shot generalization capabilities, ranging from randomized object placements to novel tasks with unseen prompts. This rigorous evaluation ensures a well-rounded assessment of the agent's ability to generalize learning beyond its training distribution.

Implications and Future Directions

The proposed formulation of task specification through multimodal prompts has significant theoretical and practical implications. By merging language understanding with visual perception in a unified model, the research enhances the flexibility and adaptability of robotic systems in handling varied tasks without specialized models. This approach could pave the way for more robust generalist robots capable of performing a wide spectrum of tasks with minimal retraining.

Looking ahead, further research could explore integration with more realistic simulation environments and expansion to include additional action primitives. The potential application of VIMA in real-world scenarios, supported by robust and adaptable object detectors, could significantly advance the field of robotic manipulation.

In summary, the paper offers a compelling method for simplifying and unifying task specifications in robotics through multimodal prompts, demonstrating promising results in model scalability and data efficiency. This research forms a solid foundation for future exploration in developing versatile and generalizable robotic systems.