Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps

TL;DR

This paper introduces PGDM, a framework for learning diverse dexterous manipulation behaviors using pre-grasps, eliminating the need for task-specific tuning, and verifies its effectiveness on a new benchmark of 50 tasks.

Contribution

Develops PGDM, a pre-grasp based exploration method that simplifies learning dexterous manipulation without task-specific engineering or supervision.

Findings

PGDM matches prior methods' performance without tuning.

Introduces TCDM, a benchmark with 50 diverse manipulation tasks.

Pre-grasp primitive effectively guides exploration in manipulation learning.

Abstract

Learning diverse dexterous manipulation behaviors with assorted objects remains an open grand challenge. While policy learning methods offer a powerful avenue to attack this problem, they require extensive per-task engineering and algorithmic tuning. This paper seeks to escape these constraints, by developing a Pre-Grasp informed Dexterous Manipulation (PGDM) framework that generates diverse dexterous manipulation behaviors, without any task-specific reasoning or hyper-parameter tuning. At the core of PGDM is a well known robotics construct, pre-grasps (i.e. the hand-pose preparing for object interaction). This simple primitive is enough to induce efficient exploration strategies for acquiring complex dexterous manipulation behaviors. To exhaustively verify these claims, we introduce TCDM, a benchmark of 50 diverse manipulation tasks defined over multiple objects and dexterous manipulators. Tasks for TCDM are defined automatically using exemplar object trajectories from various sources (animators, human behaviors, etc.), without any per-task engineering and/or supervision. Our experiments validate that PGDM's exploration strategy, induced by a surprisingly simple ingredient (single pre-grasp pose), matches the performance of prior methods, which require expensive per-task feature/reward engineering, expert supervision, and hyper-parameter tuning. For animated visualizations, trained policies, and project code, please refer to: https://pregrasps.github.io/