HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.
hub Canonical reference
Memer: Scaling up memory for robot control via experience retrieval
Canonical reference. 86% of citing Pith papers cite this work as background.
26
Pith papers citing it
Background
86% of classified citations
hub tools
citation-role summary
background 7
citation-polarity summary
years
2026 26roles
background 7representative citing papers
Introduces RoboMME-Interference benchmark showing memory-augmented VLAs improve without distractors but decay steadily as unrelated sessions accumulate in history.
AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.
A hardware-free dual-camera capture framework with ChArUco spatial unification and receding-horizon state alignment enables decoupled SE(3) manipulation and SE(2) base trajectories for diffusion policies, yielding 83.8% average success on four long-horizon household tasks.
π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.
PhysMem enables VLM-based robot planners to learn and verify physical properties through test-time interaction and hypothesis testing, raising success on a brick insertion task from 23% to 76%.
Proposes a structured concept-centric memory system for embodied agents that connects object, scene, transition, and skill memories to support coarse-to-fine retrieval and improve task performance over baselines.
KEMO is an event-driven keyframe memory system that improves VLA policy success rates by 23.6% on real dual-arm tasks by selectively preserving task-relevant history via kinematics-visual event detection and gated fusion.
CAMP learns a compressed behavioral memory from action history to enable success in long-horizon partially observable object manipulation without extra supervision, showing gains over baselines in real-robot and simulation tests.
EventVLA introduces foundational visual anchors and a Keyframe Evidence Memory module that predicts future keyframe probabilities from VLA embeddings to improve long-horizon task success by an average of 40% on 17 simulation and 4 real-world tasks.
Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.
WeaveLA improves VLA policies for repetitive robot manipulation by event-triggered cross-subtask latent memory weaving, raising success on the hardest repetition tasks from 0% to 47.8% while leaving single-execution performance unchanged.
DAM-VLA decouples per-modality temporal processing in vision-language-action models via latent buffers refreshed at sensor rates, achieving 95.2% average success versus 40.95% for synchronous baselines on seven real-world manipulation tasks while enabling 100 Hz control.
Adding recurrent memory tokens to VLA models raises success rates on partially observable manipulation tasks from 0.42 to 0.84 on training and 0.07 to 0.23 on held-out tasks while preserving performance under full observability.
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
MemoryVAM integrates a Perceiver-based Recap Compressor and Cue Gate into video action models, raising success rates on long-horizon manipulation from 5% to 42.5% on LIBERO-Mem and 75-80% on real-robot counting, spatial recall, and tracking tasks.
HiMem-WAM integrates hierarchical latent actions and boundary-aware memory gates into world action models to enhance robustness and performance on memory-dependent long-horizon robotic tasks.
MemoryVLA++ integrates a perceptual-cognitive memory bank and denoising world model into VLA models to enable temporal reasoning, yielding performance gains on manipulation benchmarks and real-robot tasks.
Wall-OSS-0.5 is a 4B VLA model pretrained across many embodiments that achieves zero-shot real-robot performance on a 17-task suite and outperforms π_0.5 after fine-tuning.
GMP selectively activates and represents memory via a gate and lightweight cross-attention, yielding 30.1% higher success on non-Markovian robotic tasks while staying competitive on Markovian ones.
HiVLA decouples VLM-based semantic planning with visual grounding from a cascaded cross-attention DiT action expert, outperforming end-to-end VLAs on long-horizon and fine-grained manipulation.
A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.
LingBot-VA combines video world modeling with policy learning via Mixture-of-Transformers, closed-loop rollouts, and asynchronous inference to improve robot manipulation in simulation and real settings.
MemoryWAM is a world action model with a hybrid memory design using recent frames, anchor frames, and gist tokens for efficient long-horizon robotic manipulation.
citing papers explorer
-
Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control
HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.
-
Benchmarking Robot Memory Under Interference
Introduces RoboMME-Interference benchmark showing memory-augmented VLAs improve without distractors but decay steadily as unrelated sessions accumulate in history.
-
AURA: Action-Gated Memory for Robot Policies at Constant VRAM
AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.
-
Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation
A hardware-free dual-camera capture framework with ChArUco spatial unification and receding-horizon state alignment enables decoupled SE(3) manipulation and SE(2) base trajectories for diffusion policies, yielding 83.8% average success on four long-horizon household tasks.
-
${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.
-
PhysMem: Scaling Test-Time Memory for Embodied Physical Reasoning
PhysMem enables VLM-based robot planners to learn and verify physical properties through test-time interaction and hypothesis testing, raising success on a brick insertion task from 23% to 76%.
-
Analytic Concept-Centric Memory for Agentic Embodied Manipulation
Proposes a structured concept-centric memory system for embodied agents that connects object, scene, transition, and skill memories to support coarse-to-fine retrieval and improve task performance over baselines.
-
KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies
KEMO is an event-driven keyframe memory system that improves VLA policy success rates by 23.6% on real dual-arm tasks by selectively preserving task-relevant history via kinematics-visual event detection and gated fusion.
-
Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation
CAMP learns a compressed behavioral memory from action history to enable success in long-horizon partially observable object manipulation without extra supervision, showing gains over baselines in real-robot and simulation tests.
-
EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
EventVLA introduces foundational visual anchors and a Keyframe Evidence Memory module that predicts future keyframe probabilities from VLA embeddings to improve long-horizon task success by an average of 40% on 17 simulation and 4 real-world tasks.
-
Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So
Flash endurance is priced via shadow price η making placement cost-optimal for any sign of value-write correlation χ, with χ positive only in recurrent long-horizon manipulation and the budget binding only on low-endurance commodity hardware.
-
WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation
WeaveLA improves VLA policies for repetitive robot manipulation by event-triggered cross-subtask latent memory weaving, raising success on the hardest repetition tasks from 0% to 47.8% while leaving single-execution performance unchanged.
-
DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
DAM-VLA decouples per-modality temporal processing in vision-language-action models via latent buffers refreshed at sensor rates, achieving 95.2% average success versus 40.95% for synchronous baselines on seven real-world manipulation tasks while enabling 100 Hz control.
-
$\mu$VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models
Adding recurrent memory tokens to VLA models raises success rates on partially observable manipulation tasks from 0.42 to 0.84 on training and 0.07 to 0.23 on held-out tasks while preserving performance under full observability.
-
HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
-
MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation
MemoryVAM integrates a Perceiver-based Recap Compressor and Cue Gate into video action models, raising success rates on long-horizon manipulation from 5% to 42.5% on LIBERO-Mem and 75-80% on real-robot counting, spatial recall, and tracking tasks.
-
HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation
HiMem-WAM integrates hierarchical latent actions and boundary-aware memory gates into world action models to enhance robustness and performance on memory-dependent long-horizon robotic tasks.
-
MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
MemoryVLA++ integrates a perceptual-cognitive memory bank and denoising world model into VLA models to enable temporal reasoning, yielding performance gains on manipulation benchmarks and real-robot tasks.
-
Wall-OSS-0.5 Technical Report
Wall-OSS-0.5 is a 4B VLA model pretrained across many embodiments that achieves zero-shot real-robot performance on a 17-task suite and outperforms π_0.5 after fine-tuning.
-
Gated Memory Policy
GMP selectively activates and represents memory via a gate and lightweight cross-attention, yielding 30.1% higher success on non-Markovian robotic tasks while staying competitive on Markovian ones.
-
HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
HiVLA decouples VLM-based semantic planning with visual grounding from a cascaded cross-attention DiT action expert, outperforming end-to-end VLAs on long-horizon and fine-grained manipulation.
-
Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
A dual VLM-VLA framework for long-horizon robot manipulation achieves 32.4% success on RMBench tasks versus 9.8% for the strongest baseline via structured memory and closed-loop adaptive replanning.
-
Causal World Modeling for Robot Control
LingBot-VA combines video world modeling with policy learning via Mixture-of-Transformers, closed-loop rollouts, and asynchronous inference to improve robot manipulation in simulation and real settings.
-
MemoryWAM: Efficient World Action Modeling with Persistent Memory
MemoryWAM is a world action model with a hybrid memory design using recent frames, anchor frames, and gist tokens for efficient long-horizon robotic manipulation.
-
RLDX-1 Technical Report
RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.
- RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies