[Paper Review] Multi-Stage Learning for Grasp-Constrained Object Manipulation with a Simulated Panda Robot

Acs, Matthew, Zhong, Xiangnan|arXiv (Cornell University)|Sep 25, 2020

Reinforcement Learning in RoboticsComputer Science15 references166 citations

TL;DR

This paper presents robosuite, a MuJoCo-based modular simulation framework and benchmark for robot learning, offering standardized tasks, procedural environment generation, and multi-modal sensing to support reproducible research.

ABSTRACT

This repository contains code and experiment assets for RAES (Reward-Aligned Expert Sequencing) and RSTB (Reward-Saturated Temporal Branching)—two lightweight reinforcement learning (RL) frameworks for long-horizon robotic manipulation without demonstrations, learned high-level controllers, or heavy task engineering. We study the robosuite [1] Stack benchmark (reach → grasp → lift/align → stack) and show that coupling continuous shaping with discrete endpoints into reward pairs produces a smoother, more learnable landscape. RAES aligns modular experts with these reward pairs (reach–grasp; lift/align–stack) and executes them sequentially. Across 10M timesteps and 5 seeds, RAES achieves the strongest performance under paired rewards, reaching 223.27 ± 28.68 mean return and 19.30% ± 2.55% success—surpassing PPO and curriculum baselines—while remaining fully RL-based (no demonstrations or hand-coded subtasks). Note: RAES and RASE (Reward Aligned Sequence of Experts) are used interchangeably in the codebase 1. Zhu Y, Wong J, Mandlekar A, Martín-Martín R, Joshi A, Lin K, Maddukuri A, Nasiriany S, Zhu Y. robosuite: A Modular Simulation Framework and Benchmark for Robot Learning. arXiv:2009.12293 [cs.RO]; 2025. Available at: https://arxiv.org/abs/2009.12293.

Motivation & Objective

• Provide a flexible, modular framework to create robotic manipulation environments and tasks.
• Offer off-the-shelf, realistic robot controllers and learning pipelines to lower entry barriers.
• Deliver standardized benchmark tasks for rigorous evaluation and reproducibility.
• Support multi-modal sensing and human demonstrations to enhance learning and data collection.
• Enable reproducible benchmarking of data-driven robotics algorithms across diverse robots and tasks.

Proposed method

• Two main APIs: Modeling APIs to define simulation environments and Simulation APIs to interface with the physics engine.
• Task composition: each Task combines a RobotModel, Arena, and Object Model to form an MJCF model for MuJoCo.
• Environment objects expose OpenAI Gym–style interfaces and configurable properties (e.g., has_renderer, horizon, reward_shaping).
• Robots, controllers, and sensors are modular, enabling plug-and-play combinations of robot arms, grippers, and control schemes.
• Sensors provide multimodal observations (RGB-D, proprioception, force-torque, etc.) and environments supply rewards and task metadata.
• I/O devices (e.g., keyboard, SpaceMouse) enable real-time teleoperation and data collection for demonstrations and debugging.
• A benchmark suite evaluates learning algorithms (e.g., SAC) on nine standardized tasks with reproducible experimental settings.

Experimental results

Research questions

• RQ1 How can a modular framework support diverse robotic manipulation tasks with reproducible benchmarking?
• RQ2 What is the impact of controller choices and action spaces on learning efficiency in manipulation tasks?
• RQ3 Can a standardized environment suite enable fair comparisons and progress tracking for data-driven robotics methods?
• RQ4 How do multi-modal sensing and human demonstrations integrate into learning pipelines in simulated robotics?
• RQ5 What are the practical configurations (robots, grippers, controllers) that maximize learning performance across tasks?

Key findings

• robosuite v1.0 provides seven robot models, eight grippers, six controllers, and nine standardized tasks.
• Benchmarking with SAC shows it solves three out of nine environments (Block Lifting, Door Opening, Two Arm Peg-in-Hole) under specific settings.
• Operational space controllers improve learning efficiency compared to joint-velocity controllers in at least some tasks, suggesting advantages of task-space exploration.
• Two-arm tasks demonstrate coordinated manipulation capabilities with multiple robots (Panda or Sawyer) in the same framework.
• The framework supports procedural environment generation and a repository for reproducible experimental results.