Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents
This paper introduces ValuePlanner, a hierarchical cognitive architecture that combines LLM-based value reasoning with classical planning to enable embodied agents to execute stable, self-directed, long-horizon behaviors by resolving motivational conflicts, validated through a novel value-centric evaluation suite in the TongSim environment.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine you have a robot helper in your house. Right now, most of these robots are like very obedient but slightly confused interns. If you tell them, "Go clean the kitchen," they do it. If you say, "Go make coffee," they do that too. But if you just walk away and leave them alone? They usually just stand there, waiting for a new order. They don't know what to do on their own.
This paper introduces a new kind of robot brain called ValuePlanner. Instead of waiting for orders, this robot has its own "personality" and a set of internal values that tell it what matters most. It's like giving the robot a moral compass and a to-do list it writes for itself.
Here is how it works, broken down into simple parts:
1. The Problem: The "What" vs. The "How"
The authors realized that making a robot act on its own is hard because there are two different jobs:
- The "What" (High-Level Thinking): Deciding what to do next based on what feels important. (e.g., "I should probably tidy up because I like order.")
- The "How" (Low-Level Action): Figuring out the specific steps to do it without breaking anything. (e.g., "Pick up the cup, walk to the sink, turn on the water...")
Current robots often try to do both at once with one big brain (usually a Large Language Model). The problem is that these big brains sometimes "hallucinate" (make things up) or forget the rules of physics (like trying to walk through a wall).
2. The Solution: A Two-Part Team
The authors built ValuePlanner, which splits the job into two specialized team members who talk to each other:
- The "Visionary" (The LLM): This is the creative part. It looks at the robot's internal "values" (like "I love a clean room" or "I want to be safe") and decides on a Goal. It doesn't worry about the tiny steps; it just says, "Let's make the room tidy."
- The "Foreman" (The Symbolic Planner): This is the strict, logical part. It takes the Visionary's goal and checks the rules of the house. It says, "Okay, to make the room tidy, we need to pick up the trash, then put it in the bin. We cannot walk through the wall." It creates a step-by-step plan that is guaranteed to work physically.
The Magic Loop:
If the Foreman says, "Hey, that plan won't work because the trash can is full," the Visionary doesn't just give up. It gets a second opinion from a Critic (a third brain that acts like a coach). The Critic says, "That plan was too messy. Let's try a different goal." They keep refining the plan until it's perfect.
3. The "Values" (The Robot's Personality)
How does the robot know what to do when it has no boss? It uses a system based on human psychology (specifically, the Schwartz theory of values).
Imagine the robot has a dial with 7 settings, like a volume knob for different feelings:
- Security: "I want to be safe and comfortable."
- Stewardship: "I want to take care of my home and keep it clean."
- Hedonism: "I want to relax and have fun."
- Achievement: "I want to get things done."
If the robot is "hungry" (low energy), it might prioritize Security (eat something). If it's full but the room is messy, it might prioritize Stewardship (clean up). If it's bored, it might prioritize Hedonism (read a book).
The cool part is that the robot can arbitrate conflicts.
- Scenario: The room is messy, but there's a vase on the edge of the table that might fall.
- Old Robot: Might just clean the mess and knock over the vase.
- ValuePlanner: Weighs the values. "Security" (don't break the vase) is more important right now than "Stewardship" (cleaning). So, it moves the vase first, then cleans the mess. It makes a smart trade-off.
4. How They Tested It
They put this robot in a virtual house (called TongSim) and watched it for a long time without giving it any commands.
- The Result: The robot didn't just sit there. It started cleaning, organizing, and relaxing on its own.
- The Comparison: They compared it to other robots that just follow orders or react to basic needs (like "I'm hungry, I eat"). The ValuePlanner was much better at making a coherent, long-term plan that felt like a real person living in a house, rather than a machine just checking boxes.
5. The Bottom Line
This paper doesn't just say "robots can do tasks." It says, "Robots can have a 'why'."
By separating the creative "what should I do?" from the logical "how do I do it?", and by giving the robot a set of internal values to guide its choices, the authors created an agent that can act proactively. It's not just following a script; it's making decisions based on what it thinks is important, just like a human does when they decide to clean their room even though no one asked them to.
In short: They taught a robot to have a personality and a plan, so it can live its own life in a house, rather than just waiting for you to tell it what to do next.
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