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

Puhao Li, Tengyu Liu, Yuyang Li, Muzhi Han, Haoran Geng, Shu Wang, Yixin Zhu, Song-Chun Zhu, Siyuan Huang

TL;DR

Ag2Manip tackles the problem of learning novel robotic manipulation skills without expert demonstrations by introducing agent-agnostic visual and action representations. The visual module obscures embodiment cues from human videos and uses time-contrastive pre-training to focus on task dynamics, while the action module abstracts robot motions to a universal proxy agent with exploration and interaction phases, followed by IK-based retargeting to the robot. A novel reward shaping strategy based on embedding similarity and an importance weighting guides exploration, and proximal policy optimization trains the proxy policy; later, Ik-based retargeting translates proxy actions into robot trajectories. Across 24 simulated tasks and real-world tests, Ag2Manip achieves up to 78.7% simulated success (vs baselines around 11–19%) and increases imitation-learning success from 50% to 77.5%, demonstrating strong generalization and practical impact for autonomous manipulation. This work significantly advances embodiment-agnostic learning, enabling robots to acquire diverse manipulation skills with reduced task-specific supervision and improved transfer to real hardware.

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.

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

TL;DR

Ag2Manip tackles the problem of learning novel robotic manipulation skills without expert demonstrations by introducing agent-agnostic visual and action representations. The visual module obscures embodiment cues from human videos and uses time-contrastive pre-training to focus on task dynamics, while the action module abstracts robot motions to a universal proxy agent with exploration and interaction phases, followed by IK-based retargeting to the robot. A novel reward shaping strategy based on embedding similarity and an importance weighting guides exploration, and proximal policy optimization trains the proxy policy; later, Ik-based retargeting translates proxy actions into robot trajectories. Across 24 simulated tasks and real-world tests, Ag2Manip achieves up to 78.7% simulated success (vs baselines around 11–19%) and increases imitation-learning success from 50% to 77.5%, demonstrating strong generalization and practical impact for autonomous manipulation. This work significantly advances embodiment-agnostic learning, enabling robots to acquire diverse manipulation skills with reduced task-specific supervision and improved transfer to real hardware.

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.
Paper Structure (18 sections, 4 equations, 3 figures, 3 tables)

This paper contains 18 sections, 4 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Learning Robotic Manipulation
  4. Reward Generation for Skill Learning
  5. Agent-Agnostic Representation
  6. Method
  7. Agent-Agnostic Visual Representation
  8. Agent-Agnostic Action Representation
  9. Reinforcement Learning and Reward Shaping
  10. Robot-Specific Action Retargeting
  11. Implementation Details
  12. Simulations and Experiments
  13. Simulation Setup
  14. Results: Simulation
  15. Results: Ablation
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Framework of Ag2Manip. Our approach is structured into three primary components: (a) learning an agent-agnostic visual representation, (b) learning abstracted skills via an agent-agnostic action representation, and (c) retargeting the abstracted skills to a robot.
  • Figure 2: Qualitative results in simulation. The top four rows are successful executions, whereas the bottom row shows failures.
  • Figure 3: Experimental setup.