# SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation

arXiv: https://arxiv.org/abs/2501.18564 · HTML: https://mlsanity.com/paper/2501.18564/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2025.

## Built on by (most cited first, 30 of 61)

- [π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities](https://mlsanity.com/paper/2604.15483/) (2026)
- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [MemER: Scaling Up Memory for Robot Control via Experience Retrieval](https://mlsanity.com/paper/2510.20328/) (2025)
- [MEM: Multi-Scale Embodied Memory for Vision Language Action Models](https://mlsanity.com/paper/2603.03596/) (2026)
- [RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design](https://mlsanity.com/paper/2603.01229/) (2026)
- [Vision Language Action Models in Robotic Manipulation: A Systematic Review](https://mlsanity.com/paper/2507.10672/) (2025)
- [Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning](https://mlsanity.com/paper/2502.10550/) (2025)
- [Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization](https://mlsanity.com/paper/2505.15660/) (2025)
- [ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries](https://mlsanity.com/paper/2603.12942/) (2026)
- [RLDX-1 Technical Report](https://mlsanity.com/paper/2605.03269/) (2026)
- [EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies](https://mlsanity.com/paper/2606.20092/) (2026)
- [Rethinking Progression of Memory State in Robotic Manipulation: An Object-Centric Perspective](https://mlsanity.com/paper/2511.11478/) (2025)
- [Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy](https://mlsanity.com/paper/2512.11218/) (2025)
- [Gated Memory Policy: In-Context Memorization and Adaptation](https://mlsanity.com/paper/2604.18933/) (2026)
- [Wall-OSS-0.5 Technical Report](https://mlsanity.com/paper/2605.30877/) (2026)
- [MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models](https://mlsanity.com/paper/2606.09827/) (2026)
- [Gaze-Regularized Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2603.23202/) (2026)
- [Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining](https://mlsanity.com/paper/2603.01465/) (2026)
- [HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation](https://mlsanity.com/paper/2606.10363/) (2026)
- [One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory](https://mlsanity.com/paper/2505.23617/) (2025)
- [Focusing on What Matters: Object-Agent-centric Tokenization for Vision Language Action models](https://mlsanity.com/paper/2509.23655/) (2025)
- [ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems](https://mlsanity.com/paper/2510.07151/) (2025)
- [RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics](https://mlsanity.com/paper/2603.12939/) (2026)
- [NativeMEM: Native Memory Compression for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2607.06678/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [SpatialActor: Exploring Disentangled Spatial Representations for Robust Robotic Manipulation](https://mlsanity.com/paper/2511.09555/) (2025)
- [KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies](https://mlsanity.com/paper/2606.23589/) (2026)
- [DIM-WAM: World-Action Modeling with Diverse Historical Event Memory](https://mlsanity.com/paper/2606.27677/) (2026)
- [Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation](https://mlsanity.com/paper/2603.24576/) (2026)
- [Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges](https://mlsanity.com/paper/2508.17449/) (2025)

## Abstract (arXiv)

Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in generalization to complex environmental variations and addressing memory-dependent tasks. To bridge this gap, we introduce SAM2Act, a multi-view robotic transformer-based policy that leverages multi-resolution upsampling with visual representations from large-scale foundation model. SAM2Act achieves a state-of-the-art average success rate of 86.8% across 18 tasks in the RLBench benchmark, and demonstrates robust generalization on The Colosseum benchmark, with only a 4.3% performance gap under diverse environmental perturbations. Building on this foundation, we propose SAM2Act+, a memory-based architecture inspired by SAM2, which incorporates a memory bank, an encoder, and an attention mechanism to enhance spatial memory. To address the need for evaluating memory-dependent tasks, we introduce MemoryBench, a novel benchmark designed to assess spatial memory and action recall in robotic manipulation. SAM2Act+ achieves an average success rate of 94.3% on memory-based tasks in MemoryBench, significantly outperforming existing approaches and pushing the boundaries of memory-based robotic systems. Project page: sam2act.github.io.