Walker Byrnes

Interested in learning more? Check out our project page below!

Project Page: plan-with-climb.github.io
Paper: arxiv.org/pdf/2410.13756
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LLMs by themselves are not perfect zero-shot solvers and can sometimes misinterpret or insufficiently describe the dynamics of an environment. CLIMB empowers these systems to explore, identify, and correct these biases to solve complex tasks.

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We develop BlocksWorld++, a curriculum of block stacking and manipulation tasks that showcases CLIMB’s ability to generalize and reuse task primitives. We implemented BlocksWorld++ in logical PDDL, IsaacSim, and a real tabletop environment to enable evaluations at multiple levels of fidelity.

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When given multiple tasks in the same environment, CLIMB caches and reuses the model generated on previous tasks when solving new ones, adding capabilities with each task completed. Given a diverse curriculum CLIMB can solve tasks more efficiently by leveraging knowledge it gained previously.

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With CLIMB, all the user needs to provide is a description of the environment and a list of tasks to accomplish. CLIMB builds an estimated world model of its domain in PDDL, calls a symbolic planner to determine an initial task plan, and attempts to solve the task.

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We are thrilled to announce that our paper, Language-Guided Continual Learning for Task Planning with Iterative Model Building (CLIMB), was accepted to #ICRA2025!

CLIMB is a robot task planner that builds a world model by iteratively executing and observing tasks.

📃 plan-with-climb.github.io
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How can robots reliably place objects in diverse real-world tasks?

🤖🔍 Placement is hard! - objects vary in shape & placement modes (such as stacking, hanging, insertion)

AnyPlace predicts placement poses of unseen objects in real-world with ony using synthetic training data!

Read on👇