title: Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with
    Reinforcement Learning
summary: Memory is crucial for enabling agents to tackle complex tasks with temporal
  and spatial dependencies. While many reinforcement learning (RL) algorithms
  incorporate memory, the field lacks a universal benchmark to assess an agent's
  memory capabilities across diverse scenarios. This gap is particularly evident
  in tabletop robotic manipulation, where memory is essential for solving tasks
  with partial observability and ensuring robust performance, yet no standardized
  benchmarks exist. To address this, we introduce MIKASA (Memory-Intensive Skills
  Assessment Suite for Agents), a comprehensive benchmark for memory RL, with
  three key contributions: (1) we propose a comprehensive classification
  framework for memory-intensive RL tasks, (2) we collect MIKASA-Base - a unified
  benchmark that enables systematic evaluation of memory-enhanced agents across
  diverse scenarios, and (3) we develop MIKASA-Robo - a novel benchmark of 32
  carefully designed memory-intensive tasks that assess memory capabilities in
  tabletop robotic manipulation. Our contributions establish a unified framework
  for advancing memory RL research, driving the development of more reliable
  systems for real-world applications. The code is available at
  https://sites.google.com/view/memorybenchrobots/.
paper_authors._id: 67b478517fa6ecaa21d1498d
paper_authors.name: Egor Cherepanov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T16:39:34.993Z
paper_authors._id: 6668687caee0993c95b0eb81
paper_authors.avatarUrl: /avatars/301fe1f395e0a129b1c9785868fa9858.svg
paper_authors.fullname: Egor Cherepanov
paper_authors.type: user
paper_authors.user: avanturist
paper_authors._id: 67b478517fa6ecaa21d1498e
paper_authors.name: Nikita Kachaev
paper_authors._id: 67b478517fa6ecaa21d1498f
paper_authors.name: Alexey K. Kovalev
paper_authors._id: 67b478517fa6ecaa21d14990
paper_authors.name: Aleksandr I. Panov
paper_title: Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with
    Reinforcement Learning
paper_summary: Memory is crucial for enabling agents to tackle complex tasks with temporal
  and spatial dependencies. While many reinforcement learning (RL) algorithms
  incorporate memory, the field lacks a universal benchmark to assess an agent's
  memory capabilities across diverse scenarios. This gap is particularly evident
  in tabletop robotic manipulation, where memory is essential for solving tasks
  with partial observability and ensuring robust performance, yet no standardized
  benchmarks exist. To address this, we introduce MIKASA (Memory-Intensive Skills
  Assessment Suite for Agents), a comprehensive benchmark for memory RL, with
  three key contributions: (1) we propose a comprehensive classification
  framework for memory-intensive RL tasks, (2) we collect MIKASA-Base - a unified
  benchmark that enables systematic evaluation of memory-enhanced agents across
  diverse scenarios, and (3) we develop MIKASA-Robo - a novel benchmark of 32
  carefully designed memory-intensive tasks that assess memory capabilities in
  tabletop robotic manipulation. Our contributions establish a unified framework
  for advancing memory RL research, driving the development of more reliable
  systems for real-world applications. The code is available at
  https://sites.google.com/view/memorybenchrobots/.
paper_ai_keywords: reinforcement learning (RL) memory RL memory-intensive RL tasks partial observability MIKASA (Memory-Intensive Skills Assessment Suite for Agents) MIKASA-Base systematic evaluation memory-enhanced agents MIKASA-Robo tabletop robotic manipulation unified framework reliable systems