This document introduces Gemini Robotics-ER 1.5, a vision-language model (VLM) specifically engineered to bring Gemini's agentic reasoning capabilities to robotics. It enables robots to interpret complex visual data, perform spatial reasoning, plan actions from natural language commands, and orchestrate long-horizon tasks by sequencing calls to existing robot APIs. The model aims to simplify human-robot interaction and increase robot autonomy by allowing them to reason, adapt, and respond in dynamic environments. It offers functionalities like object detection, tracking, trajectory planning, and contextual reasoning, while emphasizing the importance of user responsibility for safety during its preview phase.
Summarizing Gemini Robotics-ER 1.5
0:00 Introduction to Gemini Robotics-ER 1.5 (Embodied Reasoning): This is a vision-language model (VLM) in preview, purpose-built to integrate Gemini's agentic capabilities into robotics, allowing reasoning about the physical world, native tool calling, and logical step planning for missions.
Purpose and Enhancements: It enhances robotic perception and real-world interaction, providing advanced reasoning for physical problems by interpreting visual data, performing spatial reasoning, and planning actions from natural language. It orchestrates existing robot APIs to complete long-horizon tasks.
Key Applications:
Ease of Use: Assign complex tasks using natural language.
Increased Autonomy: Reason, adapt, and respond to changes in open-ended environments.
Unified Model for Robotic Tasks:
Locate and identify objects with 2D points and bounding boxes.
Understand object relationships and environmental context.
Plan grasps and trajectories for manipulation.
Interpret dynamic scenes and track objects over time from video.
Orchestrate long-horizon tasks by breaking down natural language commands into subtasks and function calls.
Enable human-robot interaction via natural language (text or speech).
Safety: While built with safety in mind, users are responsible for maintaining a safe environment around the robot, acknowledging that generative AI models can make mistakes and physical robots can cause damage. Google DeepMind is actively researching robotics safety.
Getting Started: Finding Objects in a Scene: The model can take an image and text prompt to return identified objects with normalized 2D coordinates and labels, usable with robotics APIs or other functions to generate actions.
How it Works: Gemini Robotics-ER 1.5 contextualizes and operates in the physical world via spatial understanding, processing image/video/audio input and natural language prompts to:
Understand objects, scene context, and task instructions.
Reason spatially and temporally.
Provide structured output like coordinates (points or bounding boxes).
Act agentically by sequencing subtasks and making function calls or executing generated code.
Using the Thinking Budget: A flexible thinking budget allows users to control latency vs. accuracy tradeoffs, with small budgets suitable for object detection and larger budgets for complex reasoning tasks like counting or weight estimation.
Agentic Capabilities Examples:
Pointing to Objects: Find specific or abstract objects (e.g., "fruit") and return their coordinates in images or video frames.
Tracking Objects in a Video: Analyze video frames to track objects over time, returning coordinates for specified items in each frame.
Object Detection and Bounding Boxes: Return 2D bounding boxes for identifiable objects, with instructions for unique naming of multiple instances.
Trajectories: Generate sequences of points defining a path for robot movement, such as moving a pen to an organizer.
Orchestration (Spatial Reasoning):
Making Room for a Laptop: Identify which object needs to be moved to create space for another item.
Packing a Lunch: Provide step-by-step instructions for multi-step tasks, pointing to relevant objects for each action.
Custom Robot API Calls: Orchestrate tasks by calling a sequence of user-defined robot API functions (e.g., pick-and-place operation), including reasoning for the planned actions.
Code Execution: Suggest and execute Python code for dynamic actions, such as zooming into an image region for better detail and then interpreting the magnified information.
Best Practices for Interaction:
Use clear, simple, natural, conversational language with everyday terminology in prompts.
Optimize visual input by zooming in for detail and experimenting with lighting/color.
Break down complex problems into smaller, manageable steps.
Improve accuracy by querying the model multiple times and averaging results for a "consensus."
Limitations:
Preview Status: APIs and capabilities may change; not suitable for production without thorough testing.
Latency: Can increase with complex queries, high-resolution inputs, or extensive thinking budgets.
Hallucinations: Like all LLMs, it can occasionally provide incorrect information.
Prompt Quality Dependence: Output quality relies heavily on the clarity and specificity of the input prompt.
Computational Cost: Consumes resources and incurs costs, especially with video inputs or high thinking budgets.
Input Types: Specific limitations for image, video, and audio inputs exist.
Privacy Notice: Users are responsible for ensuring consent and notification if operating Robotics Models in a manner that collects Personal Data from identifiable persons, minimizing collection where practicable.
Pricing: Refer to the pricing page for detailed information.