Voxposer简介

VoxPoser is a framework that leverages large language models (LLMs) and vision-language models (VLMs, or Vision-Language Models, are a class of artificial intelligence models designed to process and integrate visual and textual information. These models are capable of understanding and reasoning about both images (or video) and associated natural language descriptions or queries.) to generate 3D value maps, which guide motion planners in synthesizing robot trajectories for various manipulation tasks. By extracting affordances and constraints from natural language instructions, VoxPoser enables robots to perform tasks without additional training, effectively generalizing to a wide range of instructions and objects.

The process begins with the LLM interpreting a given instruction to infer the necessary actions and constraints. It then generates code that interacts with a VLM to produce 3D value maps—comprising affordance maps indicating areas of interest and constraint maps highlighting regions to avoid. These maps are grounded in the robot's observation space and serve as objective functions for motion planners. This approach allows for zero-shot synthesis of closed-loop robot trajectories, enabling robots to execute everyday manipulation tasks specified in free-form natural language.

VoxPoser has demonstrated its capabilities in both simulated and real-world environments, performing a variety of tasks such as opening drawers, hanging towels, and sorting objects. Its robustness to dynamic perturbations is enhanced through closed-loop visual feedback and model predictive control, allowing for rapid replanning in response to disturbances. Additionally, VoxPoser can efficiently learn dynamics models for contact-rich interactions from online experiences, further improving its performance in complex tasks.

In summary, VoxPoser represents a significant advancement in robotic manipulation by integrating LLMs and VLMs to interpret natural language instructions and generate actionable 3D value maps, facilitating versatile and adaptive robot behavior without the need for extensive training.

Metaphorically, comparison works quite well:

• LLM as the Brain: The Large Language Model (LLM) acts like the brain because it processes and interprets complex information, generates insights, and makes sense of abstract concepts like language and reasoning. It’s the decision-making and cognitive center that translates intentions into actions or explanations.

• VLM as the Eyes: The Vision-Language Model can be likened to the eyes because it observes and interprets the visual world. It connects what it "sees" to meaning, enabling the brain (LLM) to process it further. Essentially, it bridges perception (visual data) and understanding (language).

In this metaphor, the LLM (brain) and VLM (eyes) must work together. The VLM provides the sensory input (what’s happening or what’s present visually), and the LLM processes this input to decide, explain, or predict outcomes based on language understanding.

Example in Action:

If you give an instruction like, “Pick up the red apple from the table”:

1. VLM (Eyes): Identifies the table, detects objects on it, and locates the red apple.
2. LLM (Brain): Interprets your instruction, reasons about what needs to be done, and generates the specific steps to achieve the task.

Together, they simulate an artificial perception-action loop, much like how humans operate!

Metaphorically, the combination of the brain (LLM) and the eyes (VLM) represents what VoxPoser achieves in robotic systems. Let's break it down:

VoxPoser as the Brain + Eyes Combination

• LLM (Brain): VoxPoser uses Large Language Models to understand and interpret natural language instructions, reasoning about what needs to be done (e.g., "Place the book on the shelf").
• VLM (Eyes): VoxPoser integrates Vision-Language Models to perceive and interpret the visual environment, identifying objects, understanding spatial relationships, and grounding instructions in the robot's surroundings.

How VoxPoser Combines Them:

VoxPoser brings together these two capabilities to translate human-like understanding into robot actions:

1. Comprehension: The LLM interprets what the user wants, extracting goals and constraints.
2. Perception: The VLM links the interpreted instructions to the real-world visual context (e.g., detecting objects or determining spatial affordances).
3. Actionable Maps: VoxPoser uses this information to generate 3D value maps, which guide the robot to perform the desired actions effectively (e.g., picking, placing, or avoiding obstacles).
4. Execution: A motion planner executes the task using the insights from both the "brain" and "eyes."

Metaphor Expanded:

• VoxPoser is like the human body in action, where:
  • The LLM (brain) makes sense of what needs to be done and plans the task.
  • The VLM (eyes) perceives the environment to guide actions.
  • The robot (body) carries out the physical movements based on this combined understanding.

In short, VoxPoser is the seamless integration of the brain and eyes into a unified system for intelligent robotic manipulation. It allows robots to "see," "think," and "act" in a way that aligns with human instructions.