Mastering Agile Tasks with Limited Trials

2025-05-28

AI Summary

Existing approaches for highly agile, high-precision dynamic manipulation tasks—such as robotic basketball shooting—rely heavily on large-scale real-world data collection, hand-crafted reward functions, or intricate motion planning, resulting in prohibitive deployment costs. Method: This paper introduces Adaptive Diffusion Action Planning (ADAP), the first framework to integrate diffusion models with minimal online trial-and-error (<10 physical trials), eliminating the need for pre-collected large datasets, explicit system identification, or manual reward engineering. ADAP synergistically combines motion prior learning, goal-conditioned action sampling, online trajectory refinement, and closed-loop feedback adaptation to enable real-time, agile control. Contribution/Results: Deployed on a real robotic platform, ADAP achieves successful basket insertion within fewer than ten physical trials, matching human-level accuracy and efficiency. This drastically reduces deployment overhead and cost while enabling rapid task adaptation in unstructured environments.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Embodied robots nowadays can already handle many real-world manipulation tasks. However, certain other real-world tasks (e.g., shooting a basketball into a hoop) are highly agile and require high execution precision, presenting additional challenges for methods primarily designed for quasi-static manipulation tasks. This leads to increased efforts in costly data collection, laborious reward design, or complex motion planning. Such tasks, however, are far less challenging for humans. Say a novice basketball player typically needs only $sim$10 attempts to make their first successful shot, by roughly imitating a motion prior and then iteratively adjusting their motion based on the past outcomes. Inspired by this human learning paradigm, we propose the Adaptive Diffusion Action Plannin (ADAP) algorithm, a simple&scalable approach which iteratively refines its action plan by few real-world trials within a learned prior motion pattern, until reaching a specific goal. Experiments demonstrated that ADAP can learn and accomplish a wide range of goal-conditioned agile dynamic tasks with human-level precision and efficiency directly in real-world, such as throwing a basketball into the hoop in fewer than 10 trials. Project website:https://adap-robotics.github.io/ .

Problem

• Addressing agile tasks requiring high precision in robotics
• Reducing costly data collection and complex motion planning
• Achieving human-level efficiency in dynamic task learning

Innovation

• Adaptive Diffusion Action Planning algorithm
• Learned prior motion pattern refinement
• Few real-world trials for goal achievement