Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations

TL;DR

Ag2Manip introduces agent-agnostic visual and action representations to improve robotic manipulation learning, achieving significant performance gains in simulation and real-world tasks without domain-specific demonstrations.

Contribution

The paper presents a novel framework with agent-agnostic visual and action representations that enhance generalization and performance in robotic manipulation tasks.

Findings

325% performance increase in simulated benchmarks

Imitation learning success rate improved from 50% to 77.5% in real-world tests

Effective across both simulated and physical environments

Abstract

Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g., VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resulting in misaligned and ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at surmounting these challenges through two key innovations: a novel agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability; and an agent-agnostic action representation abstracting a robot's kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object. Ag2Manip's empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill, and PartManip shows a 325% increase in performance, achieved without domain-specific demonstrations. Ablation studies underline the essential contributions of the visual and action representations to this success. Extending our evaluations to the real world, Ag2Manip significantly improves imitation learning success rates from 50% to 77.5%, demonstrating its effectiveness and generalizability across both simulated and physical environments.