EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation

2026-07-13

AI Summary

This work addresses a key limitation in existing robotic manipulation approaches, which often neglect the dependence of action semantics on environmental context, resulting in noisy, redundant, and poorly structured control trajectories. To overcome this, the paper introduces EDAR—a novel framework that explicitly models the coupling between actions and their surrounding context. EDAR constructs environment-dependent action tokens by jointly embedding executable control commands with their visual outcomes in specific scenes. This representation enables the action space to capture interaction semantics rather than merely encoding command patterns. Evaluated in both simulated and real-world robotic manipulation tasks, EDAR significantly enhances downstream policy learning performance, demonstrating particularly strong gains in long-horizon manipulation scenarios.

Abstract

Learning effective action representations is critical for robotic manipulation, where raw control trajectories are often noisy, redundant, and difficult to model directly. Existing methods mainly encode the structure of the action stream itself, treating the role of actions in the environment as implicit. Yet manipulation is about changing the world: the same action segment can induce different outcomes under different scene contexts, making action semantics inherently environment-dependent. We propose EDAR, an Environment-Dependent Action Representation that grounds action tokens in both executable control structure and expected visual consequences. By coupling motor commands with their environment-conditioned effects, EDAR encourages the learned action space to capture interaction semantics rather than merely command-level patterns. Experiments on simulated and real-robot manipulation benchmarks demonstrate that EDAR improves downstream policy learning, especially in long-horizon manipulation. These results highlight the importance of grounding action representations in executable control structure and environment-conditioned visual change.

Problem

• action representation
• robotic manipulation
• environment-dependent
• visual consequences
• semantic grounding

Innovation

• Environment-Dependent Action Representation
• Action Semantics
• Visual Consequence Grounding
• Robotic Manipulation
• Executable Control Structure