Demystifying Action Space Design for Robotic Manipulation Policies

2026-02-26

AI Summary

This study addresses the lack of systematic guidance in action space design for robotic manipulation policy learning, a gap often filled by heuristic choices that compromise policy performance and stability. Conducting the first large-scale empirical investigation on a real dual-arm robot—with over 13,000 physical deployments and more than 500 model evaluations—the work systematically dissects the impact of action space design across temporal (absolute vs. incremental representations) and spatial (joint-space vs. task-space parameterizations) dimensions. Within an imitation learning framework and across diverse manipulation tasks, the findings demonstrate that incremental action representations significantly enhance performance, joint-space control improves stability, and task-space parameterization boosts generalization. The study establishes reproducible empirical guidelines and theoretical foundations for principled action space design in robotic manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The specification of the action space plays a pivotal role in imitation-based robotic manipulation policy learning, fundamentally shaping the optimization landscape of policy learning. While recent advances have focused heavily on scaling training data and model capacity, the choice of action space remains guided by ad-hoc heuristics or legacy designs, leading to an ambiguous understanding of robotic policy design philosophies. To address this ambiguity, we conducted a large-scale and systematic empirical study, confirming that the action space does have significant and complex impacts on robotic policy learning. We dissect the action design space along temporal and spatial axes, facilitating a structured analysis of how these choices govern both policy learnability and control stability. Based on 13,000+ real-world rollouts on a bimanual robot and evaluation on 500+ trained models over four scenarios, we examine the trade-offs between absolute vs. delta representations, and joint-space vs. task-space parameterizations. Our large-scale results suggest that properly designing the policy to predict delta actions consistently improves performance, while joint-space and task-space representations offer complementary strengths, favoring control stability and generalization, respectively.

Problem

• action space
• robotic manipulation
• policy learning
• imitation learning
• control stability

Innovation

• action space design
• robotic manipulation
• delta actions
• joint-space vs. task-space
• policy learning